IT Data Visualization - Sumit 2008

35
IT Data Visualization Raffael Marty, GCIA, CISSP Chief Security Strategist @ Splunk> SUMIT, Michigan - October ‘08

Transcript of IT Data Visualization - Sumit 2008

Page 1: IT Data Visualization - Sumit 2008

IT Data Visualization

Raffael Marty GCIA CISSPChief Security Strategist Splunkgt

SUMIT Michigan - October lsquo08

bull Chief Security Strategist Splunkgt

bull Looked at logsIT data for over 10 years

- IBM Research

- Conference boards committees

bull Presenting around the world on SecViz

bull Passion for Visualization

- httpsecvizorg

- httpafterglowsourceforgenet

Raffael Marty

Applied Security VisualizationPaperback 552 pages

Publisher Addison Wesley (August 2008)ISBN 0321510100

Agendabull IT Data Visualization

- Security Visualization Dichotomy

- Research Dichotomy

bull IT Data Management

- A shifted crime landscape

bull Perimeter Threat

bull Insider Threat

bull Security Visualization Community

3

Visualization is a more effective way of IT data management and

analysis

Visualization Questionsbull Who analyzes logs

bull Who uses visualization for log analysis

bull Who has used DAVIX

bull Have you heard of SecVizorg

bull What tools are you using for log analysis

4

IT Data Visualization

Applied Security Visualization Chapter 3

What is Visualization

6

A picture is worth a thousand log records

Generate a picture from IT data

Inspire

Pose a New Question

Explore and Discover

Support Decisions

Communicate Information

Increase Efficiency

Answer a Question

Information Visualization Process

7

Capture Process Visualize

The 1st Dichotomy

bull security databull networking protocolsbull routing protocols (the Internet)bull security impactbull security policybull jargonbull use-casesbull are the end-users

8

bull types of databull perceptionbull opticsbull color theorybull depth cue theorybull interaction theory bull types of graphsbull human computer interaction

two domainsSecurity amp Visualization

Security Visualization

The Failure - New Graphs

9

The Right Thing - Reuse Graphs

10

The Failure - The Wrong Graph

11

The Right Thing - Adequate Graphs

12

The Failure - The Wrong Integration

13

bull Using proprietary data formatbull Provide parsers for various data formats

bull does not scalebull is probably buggy incomplete

bull Use wrong data access paradigm bull complex configuration

eg needs an SSH connection

usrsharemanman5launchdplist5ltxml version=10 encoding=UTF-8gtltDOCTYPE plist PUBLIC -Apple ComputerDTD PLIST 10EN httpwwwapplecomDTDsPropertyList-10dtdgtltplist version=10gtltdictgt ltkeygt_nameltkeygt ltdictgt ltkeygt_isColumnltkeygt ltstringgtYESltstringgt ltkeygt_isOutlineColumnltkeygt ltstringgtYESltstringgt ltkeygt_orderltkeygt ltstringgt0ltstringgt ltdictgt ltkeygtbsd_nameltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt62ltstringgt ltdictgt ltkeygtdetachable_driveltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt59ltstringgt ltdictgt ltkeygtdevice_manufacturerltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt41ltstringgt ltdictgt ltkeygtdevice_modelltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt42ltstringgt ltdictgt ltkeygtdevice_revisionltkeygt

bull Keep It Simple Stupidbull Use CSV inputbull Use files as inputbull Offload to other tools

bull parsersbull data conversions

The Right Thing - KISS

14

Using node sizessizesource=1sizetarget=200maxNodeSize=02

usrsharemanman5launchdplist5ltxml version=10 encoding=UTF-8gtltDOCTYPE plist PUBLIC -Apple ComputerDTD PLIST 10EN httpwwwapplecomDTDsPropertyList-10dtdgtltplist version=10gtltdictgt ltkeygt_nameltkeygt ltdictgt ltkeygt_isColumnltkeygt ltstringgtYESltstringgt ltkeygt_isOutlineColumnltkeygt ltstringgtYESltstringgt ltkeygt_orderltkeygt ltstringgt0ltstringgt ltdictgt ltkeygtbsd_nameltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt62ltstringgt ltdictgt ltkeygtdetachable_driveltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt59ltstringgt ltdictgt ltkeygtdevice_manufacturerltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt41ltstringgt ltdictgt ltkeygtdevice_modelltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt42ltstringgt ltdictgt ltkeygtdevice_revisionltkeygt

The Failure - Unnecessary Ink

15

The Right Thing - Apply Good Visualization Practices

16

bull Dont use graphics to decorate a few numbersbull Reduce data ink ratiobull Visualization principles

The 2nd Dichotomy

17

bull donrsquot understand the real impactbull get the 70 solutionbull donrsquot think bigbull no timemoney for real researchbull canrsquot scalebull work based off of a few

customerrsquos input

bull donrsquot know whatrsquos been done in industrybull donrsquot understand the use-casesbull donrsquot understand the environments

data domainbull work on simulated databull construct their own problems bull use overly complicated impractical

solutionsbull use graphs visualization where it is not

needed

Some comments are based on paper reviews from RAID 200708 VizSec 200708

Industry Academia

two worldsIndustry amp Academia

The Way Forward

18

bull Building a secviz disciplinebull Bridging the gapbull Learning the ldquootherrdquo disciplinebull More academia industry collaboration

Security Visualization

SecViz

My Focus Areas

19

bull Use-case oriented visualizationbull IT data managementbull Perimeter Threatbull Governance Risk Compliance (GRC)bull Insider Threat bull IT data visualizationbull SecVizOrgbull DAVIX

IT Data Management

A Shifted Crime Landscapebull Crimes are moving up the stack

bull Insider crime

bull Large-scale spread of many small attacks

bull Are you prepared

bull Are you monitoring enough

21

Application Layer

Transport Layer

Network Layer

Link Layer

Physical Layer

Questions are not known in advance Have the data when you need it

The IT Search Company

Configurations

Change Events

Traps amp Alerts

Scripts amp Code

Logs

What Is IT Datavarlogmessagsoptlog

etcsyslogconfetchosts

1361212533122iso org dod internet mgmt mib-2 host hrDevice hrProcessorTable hrProcessorEntry hrProcessorLoad

psnetstat

File system changesWindows Registry

multi-line files

entire files

multi-line structures

multi-line table format

hooks into the OS

Perimeter Threat

Applied Security Visualization Chapter 6

Sparklines

24

bull Data-intense design-simple word-sized graphics

bull Examples- stock price over a day- access to port 80 over the last week

Edward Tufte (2006) Beautiful Evidence Graphics Press

Average Standard Deviation

bull Java Script Implementationhttpomnipotentnetjquerysparkline

Sparklines

25

Port Source IP Destination IP

Insider Threat

Applied Security Visualization Chapter 8

Three Types of Insider Threats

27

Fraud InformationLeak

Sabotage

Example - Insider Threat Visualization

bull More and other data sources than for the traditional security use-cases

bull Insiders often have legitimate access to machines and data You need to log more than the exceptions

bull Insider crimes are often executed on the application layer You need transaction data and chatty application logs

28

bull The questions are not known in advance bull Visualization provokes questions and

helps find answersbull Dynamic nature of fraud bull Problem for static algorithmsbull Bandits quickly adapt to fixed threshold-

based detection systemsbull Looking for any unusual patterns

User Activity

High ratio of failed logins

29

Color indicates failed logins

30

Security VisualizationCommunity

SecViz - Security VisualizationThis is a place to share discuss challenge and learn about

security visualization

Data Analysis and Visualization Linuxdavixsecvizorg

D

V

X

ToolsCapture

- Network tools

Argus

Snort

Wireshark

- Logging

syslog-ng

- Fetching data

wget

ftp

scp

Processing

- Shell tools

awk grep sed

- Graphic preprocessing

Afterglow

LGL

- Date enrichment

geoiplookup

whoisgwhois

Visualization

- Network Traffic

EtherApe

InetVis

tnv

- Generic

Afterglow

Treemap

Mondrian

R Project

Non-concluding list of tools

Thank You

raffy splunk com

Page 2: IT Data Visualization - Sumit 2008

bull Chief Security Strategist Splunkgt

bull Looked at logsIT data for over 10 years

- IBM Research

- Conference boards committees

bull Presenting around the world on SecViz

bull Passion for Visualization

- httpsecvizorg

- httpafterglowsourceforgenet

Raffael Marty

Applied Security VisualizationPaperback 552 pages

Publisher Addison Wesley (August 2008)ISBN 0321510100

Agendabull IT Data Visualization

- Security Visualization Dichotomy

- Research Dichotomy

bull IT Data Management

- A shifted crime landscape

bull Perimeter Threat

bull Insider Threat

bull Security Visualization Community

3

Visualization is a more effective way of IT data management and

analysis

Visualization Questionsbull Who analyzes logs

bull Who uses visualization for log analysis

bull Who has used DAVIX

bull Have you heard of SecVizorg

bull What tools are you using for log analysis

4

IT Data Visualization

Applied Security Visualization Chapter 3

What is Visualization

6

A picture is worth a thousand log records

Generate a picture from IT data

Inspire

Pose a New Question

Explore and Discover

Support Decisions

Communicate Information

Increase Efficiency

Answer a Question

Information Visualization Process

7

Capture Process Visualize

The 1st Dichotomy

bull security databull networking protocolsbull routing protocols (the Internet)bull security impactbull security policybull jargonbull use-casesbull are the end-users

8

bull types of databull perceptionbull opticsbull color theorybull depth cue theorybull interaction theory bull types of graphsbull human computer interaction

two domainsSecurity amp Visualization

Security Visualization

The Failure - New Graphs

9

The Right Thing - Reuse Graphs

10

The Failure - The Wrong Graph

11

The Right Thing - Adequate Graphs

12

The Failure - The Wrong Integration

13

bull Using proprietary data formatbull Provide parsers for various data formats

bull does not scalebull is probably buggy incomplete

bull Use wrong data access paradigm bull complex configuration

eg needs an SSH connection

usrsharemanman5launchdplist5ltxml version=10 encoding=UTF-8gtltDOCTYPE plist PUBLIC -Apple ComputerDTD PLIST 10EN httpwwwapplecomDTDsPropertyList-10dtdgtltplist version=10gtltdictgt ltkeygt_nameltkeygt ltdictgt ltkeygt_isColumnltkeygt ltstringgtYESltstringgt ltkeygt_isOutlineColumnltkeygt ltstringgtYESltstringgt ltkeygt_orderltkeygt ltstringgt0ltstringgt ltdictgt ltkeygtbsd_nameltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt62ltstringgt ltdictgt ltkeygtdetachable_driveltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt59ltstringgt ltdictgt ltkeygtdevice_manufacturerltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt41ltstringgt ltdictgt ltkeygtdevice_modelltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt42ltstringgt ltdictgt ltkeygtdevice_revisionltkeygt

bull Keep It Simple Stupidbull Use CSV inputbull Use files as inputbull Offload to other tools

bull parsersbull data conversions

The Right Thing - KISS

14

Using node sizessizesource=1sizetarget=200maxNodeSize=02

usrsharemanman5launchdplist5ltxml version=10 encoding=UTF-8gtltDOCTYPE plist PUBLIC -Apple ComputerDTD PLIST 10EN httpwwwapplecomDTDsPropertyList-10dtdgtltplist version=10gtltdictgt ltkeygt_nameltkeygt ltdictgt ltkeygt_isColumnltkeygt ltstringgtYESltstringgt ltkeygt_isOutlineColumnltkeygt ltstringgtYESltstringgt ltkeygt_orderltkeygt ltstringgt0ltstringgt ltdictgt ltkeygtbsd_nameltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt62ltstringgt ltdictgt ltkeygtdetachable_driveltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt59ltstringgt ltdictgt ltkeygtdevice_manufacturerltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt41ltstringgt ltdictgt ltkeygtdevice_modelltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt42ltstringgt ltdictgt ltkeygtdevice_revisionltkeygt

The Failure - Unnecessary Ink

15

The Right Thing - Apply Good Visualization Practices

16

bull Dont use graphics to decorate a few numbersbull Reduce data ink ratiobull Visualization principles

The 2nd Dichotomy

17

bull donrsquot understand the real impactbull get the 70 solutionbull donrsquot think bigbull no timemoney for real researchbull canrsquot scalebull work based off of a few

customerrsquos input

bull donrsquot know whatrsquos been done in industrybull donrsquot understand the use-casesbull donrsquot understand the environments

data domainbull work on simulated databull construct their own problems bull use overly complicated impractical

solutionsbull use graphs visualization where it is not

needed

Some comments are based on paper reviews from RAID 200708 VizSec 200708

Industry Academia

two worldsIndustry amp Academia

The Way Forward

18

bull Building a secviz disciplinebull Bridging the gapbull Learning the ldquootherrdquo disciplinebull More academia industry collaboration

Security Visualization

SecViz

My Focus Areas

19

bull Use-case oriented visualizationbull IT data managementbull Perimeter Threatbull Governance Risk Compliance (GRC)bull Insider Threat bull IT data visualizationbull SecVizOrgbull DAVIX

IT Data Management

A Shifted Crime Landscapebull Crimes are moving up the stack

bull Insider crime

bull Large-scale spread of many small attacks

bull Are you prepared

bull Are you monitoring enough

21

Application Layer

Transport Layer

Network Layer

Link Layer

Physical Layer

Questions are not known in advance Have the data when you need it

The IT Search Company

Configurations

Change Events

Traps amp Alerts

Scripts amp Code

Logs

What Is IT Datavarlogmessagsoptlog

etcsyslogconfetchosts

1361212533122iso org dod internet mgmt mib-2 host hrDevice hrProcessorTable hrProcessorEntry hrProcessorLoad

psnetstat

File system changesWindows Registry

multi-line files

entire files

multi-line structures

multi-line table format

hooks into the OS

Perimeter Threat

Applied Security Visualization Chapter 6

Sparklines

24

bull Data-intense design-simple word-sized graphics

bull Examples- stock price over a day- access to port 80 over the last week

Edward Tufte (2006) Beautiful Evidence Graphics Press

Average Standard Deviation

bull Java Script Implementationhttpomnipotentnetjquerysparkline

Sparklines

25

Port Source IP Destination IP

Insider Threat

Applied Security Visualization Chapter 8

Three Types of Insider Threats

27

Fraud InformationLeak

Sabotage

Example - Insider Threat Visualization

bull More and other data sources than for the traditional security use-cases

bull Insiders often have legitimate access to machines and data You need to log more than the exceptions

bull Insider crimes are often executed on the application layer You need transaction data and chatty application logs

28

bull The questions are not known in advance bull Visualization provokes questions and

helps find answersbull Dynamic nature of fraud bull Problem for static algorithmsbull Bandits quickly adapt to fixed threshold-

based detection systemsbull Looking for any unusual patterns

User Activity

High ratio of failed logins

29

Color indicates failed logins

30

Security VisualizationCommunity

SecViz - Security VisualizationThis is a place to share discuss challenge and learn about

security visualization

Data Analysis and Visualization Linuxdavixsecvizorg

D

V

X

ToolsCapture

- Network tools

Argus

Snort

Wireshark

- Logging

syslog-ng

- Fetching data

wget

ftp

scp

Processing

- Shell tools

awk grep sed

- Graphic preprocessing

Afterglow

LGL

- Date enrichment

geoiplookup

whoisgwhois

Visualization

- Network Traffic

EtherApe

InetVis

tnv

- Generic

Afterglow

Treemap

Mondrian

R Project

Non-concluding list of tools

Thank You

raffy splunk com

Page 3: IT Data Visualization - Sumit 2008

Agendabull IT Data Visualization

- Security Visualization Dichotomy

- Research Dichotomy

bull IT Data Management

- A shifted crime landscape

bull Perimeter Threat

bull Insider Threat

bull Security Visualization Community

3

Visualization is a more effective way of IT data management and

analysis

Visualization Questionsbull Who analyzes logs

bull Who uses visualization for log analysis

bull Who has used DAVIX

bull Have you heard of SecVizorg

bull What tools are you using for log analysis

4

IT Data Visualization

Applied Security Visualization Chapter 3

What is Visualization

6

A picture is worth a thousand log records

Generate a picture from IT data

Inspire

Pose a New Question

Explore and Discover

Support Decisions

Communicate Information

Increase Efficiency

Answer a Question

Information Visualization Process

7

Capture Process Visualize

The 1st Dichotomy

bull security databull networking protocolsbull routing protocols (the Internet)bull security impactbull security policybull jargonbull use-casesbull are the end-users

8

bull types of databull perceptionbull opticsbull color theorybull depth cue theorybull interaction theory bull types of graphsbull human computer interaction

two domainsSecurity amp Visualization

Security Visualization

The Failure - New Graphs

9

The Right Thing - Reuse Graphs

10

The Failure - The Wrong Graph

11

The Right Thing - Adequate Graphs

12

The Failure - The Wrong Integration

13

bull Using proprietary data formatbull Provide parsers for various data formats

bull does not scalebull is probably buggy incomplete

bull Use wrong data access paradigm bull complex configuration

eg needs an SSH connection

usrsharemanman5launchdplist5ltxml version=10 encoding=UTF-8gtltDOCTYPE plist PUBLIC -Apple ComputerDTD PLIST 10EN httpwwwapplecomDTDsPropertyList-10dtdgtltplist version=10gtltdictgt ltkeygt_nameltkeygt ltdictgt ltkeygt_isColumnltkeygt ltstringgtYESltstringgt ltkeygt_isOutlineColumnltkeygt ltstringgtYESltstringgt ltkeygt_orderltkeygt ltstringgt0ltstringgt ltdictgt ltkeygtbsd_nameltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt62ltstringgt ltdictgt ltkeygtdetachable_driveltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt59ltstringgt ltdictgt ltkeygtdevice_manufacturerltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt41ltstringgt ltdictgt ltkeygtdevice_modelltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt42ltstringgt ltdictgt ltkeygtdevice_revisionltkeygt

bull Keep It Simple Stupidbull Use CSV inputbull Use files as inputbull Offload to other tools

bull parsersbull data conversions

The Right Thing - KISS

14

Using node sizessizesource=1sizetarget=200maxNodeSize=02

usrsharemanman5launchdplist5ltxml version=10 encoding=UTF-8gtltDOCTYPE plist PUBLIC -Apple ComputerDTD PLIST 10EN httpwwwapplecomDTDsPropertyList-10dtdgtltplist version=10gtltdictgt ltkeygt_nameltkeygt ltdictgt ltkeygt_isColumnltkeygt ltstringgtYESltstringgt ltkeygt_isOutlineColumnltkeygt ltstringgtYESltstringgt ltkeygt_orderltkeygt ltstringgt0ltstringgt ltdictgt ltkeygtbsd_nameltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt62ltstringgt ltdictgt ltkeygtdetachable_driveltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt59ltstringgt ltdictgt ltkeygtdevice_manufacturerltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt41ltstringgt ltdictgt ltkeygtdevice_modelltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt42ltstringgt ltdictgt ltkeygtdevice_revisionltkeygt

The Failure - Unnecessary Ink

15

The Right Thing - Apply Good Visualization Practices

16

bull Dont use graphics to decorate a few numbersbull Reduce data ink ratiobull Visualization principles

The 2nd Dichotomy

17

bull donrsquot understand the real impactbull get the 70 solutionbull donrsquot think bigbull no timemoney for real researchbull canrsquot scalebull work based off of a few

customerrsquos input

bull donrsquot know whatrsquos been done in industrybull donrsquot understand the use-casesbull donrsquot understand the environments

data domainbull work on simulated databull construct their own problems bull use overly complicated impractical

solutionsbull use graphs visualization where it is not

needed

Some comments are based on paper reviews from RAID 200708 VizSec 200708

Industry Academia

two worldsIndustry amp Academia

The Way Forward

18

bull Building a secviz disciplinebull Bridging the gapbull Learning the ldquootherrdquo disciplinebull More academia industry collaboration

Security Visualization

SecViz

My Focus Areas

19

bull Use-case oriented visualizationbull IT data managementbull Perimeter Threatbull Governance Risk Compliance (GRC)bull Insider Threat bull IT data visualizationbull SecVizOrgbull DAVIX

IT Data Management

A Shifted Crime Landscapebull Crimes are moving up the stack

bull Insider crime

bull Large-scale spread of many small attacks

bull Are you prepared

bull Are you monitoring enough

21

Application Layer

Transport Layer

Network Layer

Link Layer

Physical Layer

Questions are not known in advance Have the data when you need it

The IT Search Company

Configurations

Change Events

Traps amp Alerts

Scripts amp Code

Logs

What Is IT Datavarlogmessagsoptlog

etcsyslogconfetchosts

1361212533122iso org dod internet mgmt mib-2 host hrDevice hrProcessorTable hrProcessorEntry hrProcessorLoad

psnetstat

File system changesWindows Registry

multi-line files

entire files

multi-line structures

multi-line table format

hooks into the OS

Perimeter Threat

Applied Security Visualization Chapter 6

Sparklines

24

bull Data-intense design-simple word-sized graphics

bull Examples- stock price over a day- access to port 80 over the last week

Edward Tufte (2006) Beautiful Evidence Graphics Press

Average Standard Deviation

bull Java Script Implementationhttpomnipotentnetjquerysparkline

Sparklines

25

Port Source IP Destination IP

Insider Threat

Applied Security Visualization Chapter 8

Three Types of Insider Threats

27

Fraud InformationLeak

Sabotage

Example - Insider Threat Visualization

bull More and other data sources than for the traditional security use-cases

bull Insiders often have legitimate access to machines and data You need to log more than the exceptions

bull Insider crimes are often executed on the application layer You need transaction data and chatty application logs

28

bull The questions are not known in advance bull Visualization provokes questions and

helps find answersbull Dynamic nature of fraud bull Problem for static algorithmsbull Bandits quickly adapt to fixed threshold-

based detection systemsbull Looking for any unusual patterns

User Activity

High ratio of failed logins

29

Color indicates failed logins

30

Security VisualizationCommunity

SecViz - Security VisualizationThis is a place to share discuss challenge and learn about

security visualization

Data Analysis and Visualization Linuxdavixsecvizorg

D

V

X

ToolsCapture

- Network tools

Argus

Snort

Wireshark

- Logging

syslog-ng

- Fetching data

wget

ftp

scp

Processing

- Shell tools

awk grep sed

- Graphic preprocessing

Afterglow

LGL

- Date enrichment

geoiplookup

whoisgwhois

Visualization

- Network Traffic

EtherApe

InetVis

tnv

- Generic

Afterglow

Treemap

Mondrian

R Project

Non-concluding list of tools

Thank You

raffy splunk com

Page 4: IT Data Visualization - Sumit 2008

Visualization Questionsbull Who analyzes logs

bull Who uses visualization for log analysis

bull Who has used DAVIX

bull Have you heard of SecVizorg

bull What tools are you using for log analysis

4

IT Data Visualization

Applied Security Visualization Chapter 3

What is Visualization

6

A picture is worth a thousand log records

Generate a picture from IT data

Inspire

Pose a New Question

Explore and Discover

Support Decisions

Communicate Information

Increase Efficiency

Answer a Question

Information Visualization Process

7

Capture Process Visualize

The 1st Dichotomy

bull security databull networking protocolsbull routing protocols (the Internet)bull security impactbull security policybull jargonbull use-casesbull are the end-users

8

bull types of databull perceptionbull opticsbull color theorybull depth cue theorybull interaction theory bull types of graphsbull human computer interaction

two domainsSecurity amp Visualization

Security Visualization

The Failure - New Graphs

9

The Right Thing - Reuse Graphs

10

The Failure - The Wrong Graph

11

The Right Thing - Adequate Graphs

12

The Failure - The Wrong Integration

13

bull Using proprietary data formatbull Provide parsers for various data formats

bull does not scalebull is probably buggy incomplete

bull Use wrong data access paradigm bull complex configuration

eg needs an SSH connection

usrsharemanman5launchdplist5ltxml version=10 encoding=UTF-8gtltDOCTYPE plist PUBLIC -Apple ComputerDTD PLIST 10EN httpwwwapplecomDTDsPropertyList-10dtdgtltplist version=10gtltdictgt ltkeygt_nameltkeygt ltdictgt ltkeygt_isColumnltkeygt ltstringgtYESltstringgt ltkeygt_isOutlineColumnltkeygt ltstringgtYESltstringgt ltkeygt_orderltkeygt ltstringgt0ltstringgt ltdictgt ltkeygtbsd_nameltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt62ltstringgt ltdictgt ltkeygtdetachable_driveltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt59ltstringgt ltdictgt ltkeygtdevice_manufacturerltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt41ltstringgt ltdictgt ltkeygtdevice_modelltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt42ltstringgt ltdictgt ltkeygtdevice_revisionltkeygt

bull Keep It Simple Stupidbull Use CSV inputbull Use files as inputbull Offload to other tools

bull parsersbull data conversions

The Right Thing - KISS

14

Using node sizessizesource=1sizetarget=200maxNodeSize=02

usrsharemanman5launchdplist5ltxml version=10 encoding=UTF-8gtltDOCTYPE plist PUBLIC -Apple ComputerDTD PLIST 10EN httpwwwapplecomDTDsPropertyList-10dtdgtltplist version=10gtltdictgt ltkeygt_nameltkeygt ltdictgt ltkeygt_isColumnltkeygt ltstringgtYESltstringgt ltkeygt_isOutlineColumnltkeygt ltstringgtYESltstringgt ltkeygt_orderltkeygt ltstringgt0ltstringgt ltdictgt ltkeygtbsd_nameltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt62ltstringgt ltdictgt ltkeygtdetachable_driveltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt59ltstringgt ltdictgt ltkeygtdevice_manufacturerltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt41ltstringgt ltdictgt ltkeygtdevice_modelltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt42ltstringgt ltdictgt ltkeygtdevice_revisionltkeygt

The Failure - Unnecessary Ink

15

The Right Thing - Apply Good Visualization Practices

16

bull Dont use graphics to decorate a few numbersbull Reduce data ink ratiobull Visualization principles

The 2nd Dichotomy

17

bull donrsquot understand the real impactbull get the 70 solutionbull donrsquot think bigbull no timemoney for real researchbull canrsquot scalebull work based off of a few

customerrsquos input

bull donrsquot know whatrsquos been done in industrybull donrsquot understand the use-casesbull donrsquot understand the environments

data domainbull work on simulated databull construct their own problems bull use overly complicated impractical

solutionsbull use graphs visualization where it is not

needed

Some comments are based on paper reviews from RAID 200708 VizSec 200708

Industry Academia

two worldsIndustry amp Academia

The Way Forward

18

bull Building a secviz disciplinebull Bridging the gapbull Learning the ldquootherrdquo disciplinebull More academia industry collaboration

Security Visualization

SecViz

My Focus Areas

19

bull Use-case oriented visualizationbull IT data managementbull Perimeter Threatbull Governance Risk Compliance (GRC)bull Insider Threat bull IT data visualizationbull SecVizOrgbull DAVIX

IT Data Management

A Shifted Crime Landscapebull Crimes are moving up the stack

bull Insider crime

bull Large-scale spread of many small attacks

bull Are you prepared

bull Are you monitoring enough

21

Application Layer

Transport Layer

Network Layer

Link Layer

Physical Layer

Questions are not known in advance Have the data when you need it

The IT Search Company

Configurations

Change Events

Traps amp Alerts

Scripts amp Code

Logs

What Is IT Datavarlogmessagsoptlog

etcsyslogconfetchosts

1361212533122iso org dod internet mgmt mib-2 host hrDevice hrProcessorTable hrProcessorEntry hrProcessorLoad

psnetstat

File system changesWindows Registry

multi-line files

entire files

multi-line structures

multi-line table format

hooks into the OS

Perimeter Threat

Applied Security Visualization Chapter 6

Sparklines

24

bull Data-intense design-simple word-sized graphics

bull Examples- stock price over a day- access to port 80 over the last week

Edward Tufte (2006) Beautiful Evidence Graphics Press

Average Standard Deviation

bull Java Script Implementationhttpomnipotentnetjquerysparkline

Sparklines

25

Port Source IP Destination IP

Insider Threat

Applied Security Visualization Chapter 8

Three Types of Insider Threats

27

Fraud InformationLeak

Sabotage

Example - Insider Threat Visualization

bull More and other data sources than for the traditional security use-cases

bull Insiders often have legitimate access to machines and data You need to log more than the exceptions

bull Insider crimes are often executed on the application layer You need transaction data and chatty application logs

28

bull The questions are not known in advance bull Visualization provokes questions and

helps find answersbull Dynamic nature of fraud bull Problem for static algorithmsbull Bandits quickly adapt to fixed threshold-

based detection systemsbull Looking for any unusual patterns

User Activity

High ratio of failed logins

29

Color indicates failed logins

30

Security VisualizationCommunity

SecViz - Security VisualizationThis is a place to share discuss challenge and learn about

security visualization

Data Analysis and Visualization Linuxdavixsecvizorg

D

V

X

ToolsCapture

- Network tools

Argus

Snort

Wireshark

- Logging

syslog-ng

- Fetching data

wget

ftp

scp

Processing

- Shell tools

awk grep sed

- Graphic preprocessing

Afterglow

LGL

- Date enrichment

geoiplookup

whoisgwhois

Visualization

- Network Traffic

EtherApe

InetVis

tnv

- Generic

Afterglow

Treemap

Mondrian

R Project

Non-concluding list of tools

Thank You

raffy splunk com

Page 5: IT Data Visualization - Sumit 2008

IT Data Visualization

Applied Security Visualization Chapter 3

What is Visualization

6

A picture is worth a thousand log records

Generate a picture from IT data

Inspire

Pose a New Question

Explore and Discover

Support Decisions

Communicate Information

Increase Efficiency

Answer a Question

Information Visualization Process

7

Capture Process Visualize

The 1st Dichotomy

bull security databull networking protocolsbull routing protocols (the Internet)bull security impactbull security policybull jargonbull use-casesbull are the end-users

8

bull types of databull perceptionbull opticsbull color theorybull depth cue theorybull interaction theory bull types of graphsbull human computer interaction

two domainsSecurity amp Visualization

Security Visualization

The Failure - New Graphs

9

The Right Thing - Reuse Graphs

10

The Failure - The Wrong Graph

11

The Right Thing - Adequate Graphs

12

The Failure - The Wrong Integration

13

bull Using proprietary data formatbull Provide parsers for various data formats

bull does not scalebull is probably buggy incomplete

bull Use wrong data access paradigm bull complex configuration

eg needs an SSH connection

usrsharemanman5launchdplist5ltxml version=10 encoding=UTF-8gtltDOCTYPE plist PUBLIC -Apple ComputerDTD PLIST 10EN httpwwwapplecomDTDsPropertyList-10dtdgtltplist version=10gtltdictgt ltkeygt_nameltkeygt ltdictgt ltkeygt_isColumnltkeygt ltstringgtYESltstringgt ltkeygt_isOutlineColumnltkeygt ltstringgtYESltstringgt ltkeygt_orderltkeygt ltstringgt0ltstringgt ltdictgt ltkeygtbsd_nameltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt62ltstringgt ltdictgt ltkeygtdetachable_driveltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt59ltstringgt ltdictgt ltkeygtdevice_manufacturerltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt41ltstringgt ltdictgt ltkeygtdevice_modelltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt42ltstringgt ltdictgt ltkeygtdevice_revisionltkeygt

bull Keep It Simple Stupidbull Use CSV inputbull Use files as inputbull Offload to other tools

bull parsersbull data conversions

The Right Thing - KISS

14

Using node sizessizesource=1sizetarget=200maxNodeSize=02

usrsharemanman5launchdplist5ltxml version=10 encoding=UTF-8gtltDOCTYPE plist PUBLIC -Apple ComputerDTD PLIST 10EN httpwwwapplecomDTDsPropertyList-10dtdgtltplist version=10gtltdictgt ltkeygt_nameltkeygt ltdictgt ltkeygt_isColumnltkeygt ltstringgtYESltstringgt ltkeygt_isOutlineColumnltkeygt ltstringgtYESltstringgt ltkeygt_orderltkeygt ltstringgt0ltstringgt ltdictgt ltkeygtbsd_nameltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt62ltstringgt ltdictgt ltkeygtdetachable_driveltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt59ltstringgt ltdictgt ltkeygtdevice_manufacturerltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt41ltstringgt ltdictgt ltkeygtdevice_modelltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt42ltstringgt ltdictgt ltkeygtdevice_revisionltkeygt

The Failure - Unnecessary Ink

15

The Right Thing - Apply Good Visualization Practices

16

bull Dont use graphics to decorate a few numbersbull Reduce data ink ratiobull Visualization principles

The 2nd Dichotomy

17

bull donrsquot understand the real impactbull get the 70 solutionbull donrsquot think bigbull no timemoney for real researchbull canrsquot scalebull work based off of a few

customerrsquos input

bull donrsquot know whatrsquos been done in industrybull donrsquot understand the use-casesbull donrsquot understand the environments

data domainbull work on simulated databull construct their own problems bull use overly complicated impractical

solutionsbull use graphs visualization where it is not

needed

Some comments are based on paper reviews from RAID 200708 VizSec 200708

Industry Academia

two worldsIndustry amp Academia

The Way Forward

18

bull Building a secviz disciplinebull Bridging the gapbull Learning the ldquootherrdquo disciplinebull More academia industry collaboration

Security Visualization

SecViz

My Focus Areas

19

bull Use-case oriented visualizationbull IT data managementbull Perimeter Threatbull Governance Risk Compliance (GRC)bull Insider Threat bull IT data visualizationbull SecVizOrgbull DAVIX

IT Data Management

A Shifted Crime Landscapebull Crimes are moving up the stack

bull Insider crime

bull Large-scale spread of many small attacks

bull Are you prepared

bull Are you monitoring enough

21

Application Layer

Transport Layer

Network Layer

Link Layer

Physical Layer

Questions are not known in advance Have the data when you need it

The IT Search Company

Configurations

Change Events

Traps amp Alerts

Scripts amp Code

Logs

What Is IT Datavarlogmessagsoptlog

etcsyslogconfetchosts

1361212533122iso org dod internet mgmt mib-2 host hrDevice hrProcessorTable hrProcessorEntry hrProcessorLoad

psnetstat

File system changesWindows Registry

multi-line files

entire files

multi-line structures

multi-line table format

hooks into the OS

Perimeter Threat

Applied Security Visualization Chapter 6

Sparklines

24

bull Data-intense design-simple word-sized graphics

bull Examples- stock price over a day- access to port 80 over the last week

Edward Tufte (2006) Beautiful Evidence Graphics Press

Average Standard Deviation

bull Java Script Implementationhttpomnipotentnetjquerysparkline

Sparklines

25

Port Source IP Destination IP

Insider Threat

Applied Security Visualization Chapter 8

Three Types of Insider Threats

27

Fraud InformationLeak

Sabotage

Example - Insider Threat Visualization

bull More and other data sources than for the traditional security use-cases

bull Insiders often have legitimate access to machines and data You need to log more than the exceptions

bull Insider crimes are often executed on the application layer You need transaction data and chatty application logs

28

bull The questions are not known in advance bull Visualization provokes questions and

helps find answersbull Dynamic nature of fraud bull Problem for static algorithmsbull Bandits quickly adapt to fixed threshold-

based detection systemsbull Looking for any unusual patterns

User Activity

High ratio of failed logins

29

Color indicates failed logins

30

Security VisualizationCommunity

SecViz - Security VisualizationThis is a place to share discuss challenge and learn about

security visualization

Data Analysis and Visualization Linuxdavixsecvizorg

D

V

X

ToolsCapture

- Network tools

Argus

Snort

Wireshark

- Logging

syslog-ng

- Fetching data

wget

ftp

scp

Processing

- Shell tools

awk grep sed

- Graphic preprocessing

Afterglow

LGL

- Date enrichment

geoiplookup

whoisgwhois

Visualization

- Network Traffic

EtherApe

InetVis

tnv

- Generic

Afterglow

Treemap

Mondrian

R Project

Non-concluding list of tools

Thank You

raffy splunk com

Page 6: IT Data Visualization - Sumit 2008

What is Visualization

6

A picture is worth a thousand log records

Generate a picture from IT data

Inspire

Pose a New Question

Explore and Discover

Support Decisions

Communicate Information

Increase Efficiency

Answer a Question

Information Visualization Process

7

Capture Process Visualize

The 1st Dichotomy

bull security databull networking protocolsbull routing protocols (the Internet)bull security impactbull security policybull jargonbull use-casesbull are the end-users

8

bull types of databull perceptionbull opticsbull color theorybull depth cue theorybull interaction theory bull types of graphsbull human computer interaction

two domainsSecurity amp Visualization

Security Visualization

The Failure - New Graphs

9

The Right Thing - Reuse Graphs

10

The Failure - The Wrong Graph

11

The Right Thing - Adequate Graphs

12

The Failure - The Wrong Integration

13

bull Using proprietary data formatbull Provide parsers for various data formats

bull does not scalebull is probably buggy incomplete

bull Use wrong data access paradigm bull complex configuration

eg needs an SSH connection

usrsharemanman5launchdplist5ltxml version=10 encoding=UTF-8gtltDOCTYPE plist PUBLIC -Apple ComputerDTD PLIST 10EN httpwwwapplecomDTDsPropertyList-10dtdgtltplist version=10gtltdictgt ltkeygt_nameltkeygt ltdictgt ltkeygt_isColumnltkeygt ltstringgtYESltstringgt ltkeygt_isOutlineColumnltkeygt ltstringgtYESltstringgt ltkeygt_orderltkeygt ltstringgt0ltstringgt ltdictgt ltkeygtbsd_nameltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt62ltstringgt ltdictgt ltkeygtdetachable_driveltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt59ltstringgt ltdictgt ltkeygtdevice_manufacturerltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt41ltstringgt ltdictgt ltkeygtdevice_modelltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt42ltstringgt ltdictgt ltkeygtdevice_revisionltkeygt

bull Keep It Simple Stupidbull Use CSV inputbull Use files as inputbull Offload to other tools

bull parsersbull data conversions

The Right Thing - KISS

14

Using node sizessizesource=1sizetarget=200maxNodeSize=02

usrsharemanman5launchdplist5ltxml version=10 encoding=UTF-8gtltDOCTYPE plist PUBLIC -Apple ComputerDTD PLIST 10EN httpwwwapplecomDTDsPropertyList-10dtdgtltplist version=10gtltdictgt ltkeygt_nameltkeygt ltdictgt ltkeygt_isColumnltkeygt ltstringgtYESltstringgt ltkeygt_isOutlineColumnltkeygt ltstringgtYESltstringgt ltkeygt_orderltkeygt ltstringgt0ltstringgt ltdictgt ltkeygtbsd_nameltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt62ltstringgt ltdictgt ltkeygtdetachable_driveltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt59ltstringgt ltdictgt ltkeygtdevice_manufacturerltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt41ltstringgt ltdictgt ltkeygtdevice_modelltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt42ltstringgt ltdictgt ltkeygtdevice_revisionltkeygt

The Failure - Unnecessary Ink

15

The Right Thing - Apply Good Visualization Practices

16

bull Dont use graphics to decorate a few numbersbull Reduce data ink ratiobull Visualization principles

The 2nd Dichotomy

17

bull donrsquot understand the real impactbull get the 70 solutionbull donrsquot think bigbull no timemoney for real researchbull canrsquot scalebull work based off of a few

customerrsquos input

bull donrsquot know whatrsquos been done in industrybull donrsquot understand the use-casesbull donrsquot understand the environments

data domainbull work on simulated databull construct their own problems bull use overly complicated impractical

solutionsbull use graphs visualization where it is not

needed

Some comments are based on paper reviews from RAID 200708 VizSec 200708

Industry Academia

two worldsIndustry amp Academia

The Way Forward

18

bull Building a secviz disciplinebull Bridging the gapbull Learning the ldquootherrdquo disciplinebull More academia industry collaboration

Security Visualization

SecViz

My Focus Areas

19

bull Use-case oriented visualizationbull IT data managementbull Perimeter Threatbull Governance Risk Compliance (GRC)bull Insider Threat bull IT data visualizationbull SecVizOrgbull DAVIX

IT Data Management

A Shifted Crime Landscapebull Crimes are moving up the stack

bull Insider crime

bull Large-scale spread of many small attacks

bull Are you prepared

bull Are you monitoring enough

21

Application Layer

Transport Layer

Network Layer

Link Layer

Physical Layer

Questions are not known in advance Have the data when you need it

The IT Search Company

Configurations

Change Events

Traps amp Alerts

Scripts amp Code

Logs

What Is IT Datavarlogmessagsoptlog

etcsyslogconfetchosts

1361212533122iso org dod internet mgmt mib-2 host hrDevice hrProcessorTable hrProcessorEntry hrProcessorLoad

psnetstat

File system changesWindows Registry

multi-line files

entire files

multi-line structures

multi-line table format

hooks into the OS

Perimeter Threat

Applied Security Visualization Chapter 6

Sparklines

24

bull Data-intense design-simple word-sized graphics

bull Examples- stock price over a day- access to port 80 over the last week

Edward Tufte (2006) Beautiful Evidence Graphics Press

Average Standard Deviation

bull Java Script Implementationhttpomnipotentnetjquerysparkline

Sparklines

25

Port Source IP Destination IP

Insider Threat

Applied Security Visualization Chapter 8

Three Types of Insider Threats

27

Fraud InformationLeak

Sabotage

Example - Insider Threat Visualization

bull More and other data sources than for the traditional security use-cases

bull Insiders often have legitimate access to machines and data You need to log more than the exceptions

bull Insider crimes are often executed on the application layer You need transaction data and chatty application logs

28

bull The questions are not known in advance bull Visualization provokes questions and

helps find answersbull Dynamic nature of fraud bull Problem for static algorithmsbull Bandits quickly adapt to fixed threshold-

based detection systemsbull Looking for any unusual patterns

User Activity

High ratio of failed logins

29

Color indicates failed logins

30

Security VisualizationCommunity

SecViz - Security VisualizationThis is a place to share discuss challenge and learn about

security visualization

Data Analysis and Visualization Linuxdavixsecvizorg

D

V

X

ToolsCapture

- Network tools

Argus

Snort

Wireshark

- Logging

syslog-ng

- Fetching data

wget

ftp

scp

Processing

- Shell tools

awk grep sed

- Graphic preprocessing

Afterglow

LGL

- Date enrichment

geoiplookup

whoisgwhois

Visualization

- Network Traffic

EtherApe

InetVis

tnv

- Generic

Afterglow

Treemap

Mondrian

R Project

Non-concluding list of tools

Thank You

raffy splunk com

Page 7: IT Data Visualization - Sumit 2008

Information Visualization Process

7

Capture Process Visualize

The 1st Dichotomy

bull security databull networking protocolsbull routing protocols (the Internet)bull security impactbull security policybull jargonbull use-casesbull are the end-users

8

bull types of databull perceptionbull opticsbull color theorybull depth cue theorybull interaction theory bull types of graphsbull human computer interaction

two domainsSecurity amp Visualization

Security Visualization

The Failure - New Graphs

9

The Right Thing - Reuse Graphs

10

The Failure - The Wrong Graph

11

The Right Thing - Adequate Graphs

12

The Failure - The Wrong Integration

13

bull Using proprietary data formatbull Provide parsers for various data formats

bull does not scalebull is probably buggy incomplete

bull Use wrong data access paradigm bull complex configuration

eg needs an SSH connection

usrsharemanman5launchdplist5ltxml version=10 encoding=UTF-8gtltDOCTYPE plist PUBLIC -Apple ComputerDTD PLIST 10EN httpwwwapplecomDTDsPropertyList-10dtdgtltplist version=10gtltdictgt ltkeygt_nameltkeygt ltdictgt ltkeygt_isColumnltkeygt ltstringgtYESltstringgt ltkeygt_isOutlineColumnltkeygt ltstringgtYESltstringgt ltkeygt_orderltkeygt ltstringgt0ltstringgt ltdictgt ltkeygtbsd_nameltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt62ltstringgt ltdictgt ltkeygtdetachable_driveltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt59ltstringgt ltdictgt ltkeygtdevice_manufacturerltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt41ltstringgt ltdictgt ltkeygtdevice_modelltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt42ltstringgt ltdictgt ltkeygtdevice_revisionltkeygt

bull Keep It Simple Stupidbull Use CSV inputbull Use files as inputbull Offload to other tools

bull parsersbull data conversions

The Right Thing - KISS

14

Using node sizessizesource=1sizetarget=200maxNodeSize=02

usrsharemanman5launchdplist5ltxml version=10 encoding=UTF-8gtltDOCTYPE plist PUBLIC -Apple ComputerDTD PLIST 10EN httpwwwapplecomDTDsPropertyList-10dtdgtltplist version=10gtltdictgt ltkeygt_nameltkeygt ltdictgt ltkeygt_isColumnltkeygt ltstringgtYESltstringgt ltkeygt_isOutlineColumnltkeygt ltstringgtYESltstringgt ltkeygt_orderltkeygt ltstringgt0ltstringgt ltdictgt ltkeygtbsd_nameltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt62ltstringgt ltdictgt ltkeygtdetachable_driveltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt59ltstringgt ltdictgt ltkeygtdevice_manufacturerltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt41ltstringgt ltdictgt ltkeygtdevice_modelltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt42ltstringgt ltdictgt ltkeygtdevice_revisionltkeygt

The Failure - Unnecessary Ink

15

The Right Thing - Apply Good Visualization Practices

16

bull Dont use graphics to decorate a few numbersbull Reduce data ink ratiobull Visualization principles

The 2nd Dichotomy

17

bull donrsquot understand the real impactbull get the 70 solutionbull donrsquot think bigbull no timemoney for real researchbull canrsquot scalebull work based off of a few

customerrsquos input

bull donrsquot know whatrsquos been done in industrybull donrsquot understand the use-casesbull donrsquot understand the environments

data domainbull work on simulated databull construct their own problems bull use overly complicated impractical

solutionsbull use graphs visualization where it is not

needed

Some comments are based on paper reviews from RAID 200708 VizSec 200708

Industry Academia

two worldsIndustry amp Academia

The Way Forward

18

bull Building a secviz disciplinebull Bridging the gapbull Learning the ldquootherrdquo disciplinebull More academia industry collaboration

Security Visualization

SecViz

My Focus Areas

19

bull Use-case oriented visualizationbull IT data managementbull Perimeter Threatbull Governance Risk Compliance (GRC)bull Insider Threat bull IT data visualizationbull SecVizOrgbull DAVIX

IT Data Management

A Shifted Crime Landscapebull Crimes are moving up the stack

bull Insider crime

bull Large-scale spread of many small attacks

bull Are you prepared

bull Are you monitoring enough

21

Application Layer

Transport Layer

Network Layer

Link Layer

Physical Layer

Questions are not known in advance Have the data when you need it

The IT Search Company

Configurations

Change Events

Traps amp Alerts

Scripts amp Code

Logs

What Is IT Datavarlogmessagsoptlog

etcsyslogconfetchosts

1361212533122iso org dod internet mgmt mib-2 host hrDevice hrProcessorTable hrProcessorEntry hrProcessorLoad

psnetstat

File system changesWindows Registry

multi-line files

entire files

multi-line structures

multi-line table format

hooks into the OS

Perimeter Threat

Applied Security Visualization Chapter 6

Sparklines

24

bull Data-intense design-simple word-sized graphics

bull Examples- stock price over a day- access to port 80 over the last week

Edward Tufte (2006) Beautiful Evidence Graphics Press

Average Standard Deviation

bull Java Script Implementationhttpomnipotentnetjquerysparkline

Sparklines

25

Port Source IP Destination IP

Insider Threat

Applied Security Visualization Chapter 8

Three Types of Insider Threats

27

Fraud InformationLeak

Sabotage

Example - Insider Threat Visualization

bull More and other data sources than for the traditional security use-cases

bull Insiders often have legitimate access to machines and data You need to log more than the exceptions

bull Insider crimes are often executed on the application layer You need transaction data and chatty application logs

28

bull The questions are not known in advance bull Visualization provokes questions and

helps find answersbull Dynamic nature of fraud bull Problem for static algorithmsbull Bandits quickly adapt to fixed threshold-

based detection systemsbull Looking for any unusual patterns

User Activity

High ratio of failed logins

29

Color indicates failed logins

30

Security VisualizationCommunity

SecViz - Security VisualizationThis is a place to share discuss challenge and learn about

security visualization

Data Analysis and Visualization Linuxdavixsecvizorg

D

V

X

ToolsCapture

- Network tools

Argus

Snort

Wireshark

- Logging

syslog-ng

- Fetching data

wget

ftp

scp

Processing

- Shell tools

awk grep sed

- Graphic preprocessing

Afterglow

LGL

- Date enrichment

geoiplookup

whoisgwhois

Visualization

- Network Traffic

EtherApe

InetVis

tnv

- Generic

Afterglow

Treemap

Mondrian

R Project

Non-concluding list of tools

Thank You

raffy splunk com

Page 8: IT Data Visualization - Sumit 2008

The 1st Dichotomy

bull security databull networking protocolsbull routing protocols (the Internet)bull security impactbull security policybull jargonbull use-casesbull are the end-users

8

bull types of databull perceptionbull opticsbull color theorybull depth cue theorybull interaction theory bull types of graphsbull human computer interaction

two domainsSecurity amp Visualization

Security Visualization

The Failure - New Graphs

9

The Right Thing - Reuse Graphs

10

The Failure - The Wrong Graph

11

The Right Thing - Adequate Graphs

12

The Failure - The Wrong Integration

13

bull Using proprietary data formatbull Provide parsers for various data formats

bull does not scalebull is probably buggy incomplete

bull Use wrong data access paradigm bull complex configuration

eg needs an SSH connection

usrsharemanman5launchdplist5ltxml version=10 encoding=UTF-8gtltDOCTYPE plist PUBLIC -Apple ComputerDTD PLIST 10EN httpwwwapplecomDTDsPropertyList-10dtdgtltplist version=10gtltdictgt ltkeygt_nameltkeygt ltdictgt ltkeygt_isColumnltkeygt ltstringgtYESltstringgt ltkeygt_isOutlineColumnltkeygt ltstringgtYESltstringgt ltkeygt_orderltkeygt ltstringgt0ltstringgt ltdictgt ltkeygtbsd_nameltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt62ltstringgt ltdictgt ltkeygtdetachable_driveltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt59ltstringgt ltdictgt ltkeygtdevice_manufacturerltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt41ltstringgt ltdictgt ltkeygtdevice_modelltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt42ltstringgt ltdictgt ltkeygtdevice_revisionltkeygt

bull Keep It Simple Stupidbull Use CSV inputbull Use files as inputbull Offload to other tools

bull parsersbull data conversions

The Right Thing - KISS

14

Using node sizessizesource=1sizetarget=200maxNodeSize=02

usrsharemanman5launchdplist5ltxml version=10 encoding=UTF-8gtltDOCTYPE plist PUBLIC -Apple ComputerDTD PLIST 10EN httpwwwapplecomDTDsPropertyList-10dtdgtltplist version=10gtltdictgt ltkeygt_nameltkeygt ltdictgt ltkeygt_isColumnltkeygt ltstringgtYESltstringgt ltkeygt_isOutlineColumnltkeygt ltstringgtYESltstringgt ltkeygt_orderltkeygt ltstringgt0ltstringgt ltdictgt ltkeygtbsd_nameltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt62ltstringgt ltdictgt ltkeygtdetachable_driveltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt59ltstringgt ltdictgt ltkeygtdevice_manufacturerltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt41ltstringgt ltdictgt ltkeygtdevice_modelltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt42ltstringgt ltdictgt ltkeygtdevice_revisionltkeygt

The Failure - Unnecessary Ink

15

The Right Thing - Apply Good Visualization Practices

16

bull Dont use graphics to decorate a few numbersbull Reduce data ink ratiobull Visualization principles

The 2nd Dichotomy

17

bull donrsquot understand the real impactbull get the 70 solutionbull donrsquot think bigbull no timemoney for real researchbull canrsquot scalebull work based off of a few

customerrsquos input

bull donrsquot know whatrsquos been done in industrybull donrsquot understand the use-casesbull donrsquot understand the environments

data domainbull work on simulated databull construct their own problems bull use overly complicated impractical

solutionsbull use graphs visualization where it is not

needed

Some comments are based on paper reviews from RAID 200708 VizSec 200708

Industry Academia

two worldsIndustry amp Academia

The Way Forward

18

bull Building a secviz disciplinebull Bridging the gapbull Learning the ldquootherrdquo disciplinebull More academia industry collaboration

Security Visualization

SecViz

My Focus Areas

19

bull Use-case oriented visualizationbull IT data managementbull Perimeter Threatbull Governance Risk Compliance (GRC)bull Insider Threat bull IT data visualizationbull SecVizOrgbull DAVIX

IT Data Management

A Shifted Crime Landscapebull Crimes are moving up the stack

bull Insider crime

bull Large-scale spread of many small attacks

bull Are you prepared

bull Are you monitoring enough

21

Application Layer

Transport Layer

Network Layer

Link Layer

Physical Layer

Questions are not known in advance Have the data when you need it

The IT Search Company

Configurations

Change Events

Traps amp Alerts

Scripts amp Code

Logs

What Is IT Datavarlogmessagsoptlog

etcsyslogconfetchosts

1361212533122iso org dod internet mgmt mib-2 host hrDevice hrProcessorTable hrProcessorEntry hrProcessorLoad

psnetstat

File system changesWindows Registry

multi-line files

entire files

multi-line structures

multi-line table format

hooks into the OS

Perimeter Threat

Applied Security Visualization Chapter 6

Sparklines

24

bull Data-intense design-simple word-sized graphics

bull Examples- stock price over a day- access to port 80 over the last week

Edward Tufte (2006) Beautiful Evidence Graphics Press

Average Standard Deviation

bull Java Script Implementationhttpomnipotentnetjquerysparkline

Sparklines

25

Port Source IP Destination IP

Insider Threat

Applied Security Visualization Chapter 8

Three Types of Insider Threats

27

Fraud InformationLeak

Sabotage

Example - Insider Threat Visualization

bull More and other data sources than for the traditional security use-cases

bull Insiders often have legitimate access to machines and data You need to log more than the exceptions

bull Insider crimes are often executed on the application layer You need transaction data and chatty application logs

28

bull The questions are not known in advance bull Visualization provokes questions and

helps find answersbull Dynamic nature of fraud bull Problem for static algorithmsbull Bandits quickly adapt to fixed threshold-

based detection systemsbull Looking for any unusual patterns

User Activity

High ratio of failed logins

29

Color indicates failed logins

30

Security VisualizationCommunity

SecViz - Security VisualizationThis is a place to share discuss challenge and learn about

security visualization

Data Analysis and Visualization Linuxdavixsecvizorg

D

V

X

ToolsCapture

- Network tools

Argus

Snort

Wireshark

- Logging

syslog-ng

- Fetching data

wget

ftp

scp

Processing

- Shell tools

awk grep sed

- Graphic preprocessing

Afterglow

LGL

- Date enrichment

geoiplookup

whoisgwhois

Visualization

- Network Traffic

EtherApe

InetVis

tnv

- Generic

Afterglow

Treemap

Mondrian

R Project

Non-concluding list of tools

Thank You

raffy splunk com

Page 9: IT Data Visualization - Sumit 2008

The Failure - New Graphs

9

The Right Thing - Reuse Graphs

10

The Failure - The Wrong Graph

11

The Right Thing - Adequate Graphs

12

The Failure - The Wrong Integration

13

bull Using proprietary data formatbull Provide parsers for various data formats

bull does not scalebull is probably buggy incomplete

bull Use wrong data access paradigm bull complex configuration

eg needs an SSH connection

usrsharemanman5launchdplist5ltxml version=10 encoding=UTF-8gtltDOCTYPE plist PUBLIC -Apple ComputerDTD PLIST 10EN httpwwwapplecomDTDsPropertyList-10dtdgtltplist version=10gtltdictgt ltkeygt_nameltkeygt ltdictgt ltkeygt_isColumnltkeygt ltstringgtYESltstringgt ltkeygt_isOutlineColumnltkeygt ltstringgtYESltstringgt ltkeygt_orderltkeygt ltstringgt0ltstringgt ltdictgt ltkeygtbsd_nameltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt62ltstringgt ltdictgt ltkeygtdetachable_driveltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt59ltstringgt ltdictgt ltkeygtdevice_manufacturerltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt41ltstringgt ltdictgt ltkeygtdevice_modelltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt42ltstringgt ltdictgt ltkeygtdevice_revisionltkeygt

bull Keep It Simple Stupidbull Use CSV inputbull Use files as inputbull Offload to other tools

bull parsersbull data conversions

The Right Thing - KISS

14

Using node sizessizesource=1sizetarget=200maxNodeSize=02

usrsharemanman5launchdplist5ltxml version=10 encoding=UTF-8gtltDOCTYPE plist PUBLIC -Apple ComputerDTD PLIST 10EN httpwwwapplecomDTDsPropertyList-10dtdgtltplist version=10gtltdictgt ltkeygt_nameltkeygt ltdictgt ltkeygt_isColumnltkeygt ltstringgtYESltstringgt ltkeygt_isOutlineColumnltkeygt ltstringgtYESltstringgt ltkeygt_orderltkeygt ltstringgt0ltstringgt ltdictgt ltkeygtbsd_nameltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt62ltstringgt ltdictgt ltkeygtdetachable_driveltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt59ltstringgt ltdictgt ltkeygtdevice_manufacturerltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt41ltstringgt ltdictgt ltkeygtdevice_modelltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt42ltstringgt ltdictgt ltkeygtdevice_revisionltkeygt

The Failure - Unnecessary Ink

15

The Right Thing - Apply Good Visualization Practices

16

bull Dont use graphics to decorate a few numbersbull Reduce data ink ratiobull Visualization principles

The 2nd Dichotomy

17

bull donrsquot understand the real impactbull get the 70 solutionbull donrsquot think bigbull no timemoney for real researchbull canrsquot scalebull work based off of a few

customerrsquos input

bull donrsquot know whatrsquos been done in industrybull donrsquot understand the use-casesbull donrsquot understand the environments

data domainbull work on simulated databull construct their own problems bull use overly complicated impractical

solutionsbull use graphs visualization where it is not

needed

Some comments are based on paper reviews from RAID 200708 VizSec 200708

Industry Academia

two worldsIndustry amp Academia

The Way Forward

18

bull Building a secviz disciplinebull Bridging the gapbull Learning the ldquootherrdquo disciplinebull More academia industry collaboration

Security Visualization

SecViz

My Focus Areas

19

bull Use-case oriented visualizationbull IT data managementbull Perimeter Threatbull Governance Risk Compliance (GRC)bull Insider Threat bull IT data visualizationbull SecVizOrgbull DAVIX

IT Data Management

A Shifted Crime Landscapebull Crimes are moving up the stack

bull Insider crime

bull Large-scale spread of many small attacks

bull Are you prepared

bull Are you monitoring enough

21

Application Layer

Transport Layer

Network Layer

Link Layer

Physical Layer

Questions are not known in advance Have the data when you need it

The IT Search Company

Configurations

Change Events

Traps amp Alerts

Scripts amp Code

Logs

What Is IT Datavarlogmessagsoptlog

etcsyslogconfetchosts

1361212533122iso org dod internet mgmt mib-2 host hrDevice hrProcessorTable hrProcessorEntry hrProcessorLoad

psnetstat

File system changesWindows Registry

multi-line files

entire files

multi-line structures

multi-line table format

hooks into the OS

Perimeter Threat

Applied Security Visualization Chapter 6

Sparklines

24

bull Data-intense design-simple word-sized graphics

bull Examples- stock price over a day- access to port 80 over the last week

Edward Tufte (2006) Beautiful Evidence Graphics Press

Average Standard Deviation

bull Java Script Implementationhttpomnipotentnetjquerysparkline

Sparklines

25

Port Source IP Destination IP

Insider Threat

Applied Security Visualization Chapter 8

Three Types of Insider Threats

27

Fraud InformationLeak

Sabotage

Example - Insider Threat Visualization

bull More and other data sources than for the traditional security use-cases

bull Insiders often have legitimate access to machines and data You need to log more than the exceptions

bull Insider crimes are often executed on the application layer You need transaction data and chatty application logs

28

bull The questions are not known in advance bull Visualization provokes questions and

helps find answersbull Dynamic nature of fraud bull Problem for static algorithmsbull Bandits quickly adapt to fixed threshold-

based detection systemsbull Looking for any unusual patterns

User Activity

High ratio of failed logins

29

Color indicates failed logins

30

Security VisualizationCommunity

SecViz - Security VisualizationThis is a place to share discuss challenge and learn about

security visualization

Data Analysis and Visualization Linuxdavixsecvizorg

D

V

X

ToolsCapture

- Network tools

Argus

Snort

Wireshark

- Logging

syslog-ng

- Fetching data

wget

ftp

scp

Processing

- Shell tools

awk grep sed

- Graphic preprocessing

Afterglow

LGL

- Date enrichment

geoiplookup

whoisgwhois

Visualization

- Network Traffic

EtherApe

InetVis

tnv

- Generic

Afterglow

Treemap

Mondrian

R Project

Non-concluding list of tools

Thank You

raffy splunk com

Page 10: IT Data Visualization - Sumit 2008

The Right Thing - Reuse Graphs

10

The Failure - The Wrong Graph

11

The Right Thing - Adequate Graphs

12

The Failure - The Wrong Integration

13

bull Using proprietary data formatbull Provide parsers for various data formats

bull does not scalebull is probably buggy incomplete

bull Use wrong data access paradigm bull complex configuration

eg needs an SSH connection

usrsharemanman5launchdplist5ltxml version=10 encoding=UTF-8gtltDOCTYPE plist PUBLIC -Apple ComputerDTD PLIST 10EN httpwwwapplecomDTDsPropertyList-10dtdgtltplist version=10gtltdictgt ltkeygt_nameltkeygt ltdictgt ltkeygt_isColumnltkeygt ltstringgtYESltstringgt ltkeygt_isOutlineColumnltkeygt ltstringgtYESltstringgt ltkeygt_orderltkeygt ltstringgt0ltstringgt ltdictgt ltkeygtbsd_nameltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt62ltstringgt ltdictgt ltkeygtdetachable_driveltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt59ltstringgt ltdictgt ltkeygtdevice_manufacturerltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt41ltstringgt ltdictgt ltkeygtdevice_modelltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt42ltstringgt ltdictgt ltkeygtdevice_revisionltkeygt

bull Keep It Simple Stupidbull Use CSV inputbull Use files as inputbull Offload to other tools

bull parsersbull data conversions

The Right Thing - KISS

14

Using node sizessizesource=1sizetarget=200maxNodeSize=02

usrsharemanman5launchdplist5ltxml version=10 encoding=UTF-8gtltDOCTYPE plist PUBLIC -Apple ComputerDTD PLIST 10EN httpwwwapplecomDTDsPropertyList-10dtdgtltplist version=10gtltdictgt ltkeygt_nameltkeygt ltdictgt ltkeygt_isColumnltkeygt ltstringgtYESltstringgt ltkeygt_isOutlineColumnltkeygt ltstringgtYESltstringgt ltkeygt_orderltkeygt ltstringgt0ltstringgt ltdictgt ltkeygtbsd_nameltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt62ltstringgt ltdictgt ltkeygtdetachable_driveltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt59ltstringgt ltdictgt ltkeygtdevice_manufacturerltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt41ltstringgt ltdictgt ltkeygtdevice_modelltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt42ltstringgt ltdictgt ltkeygtdevice_revisionltkeygt

The Failure - Unnecessary Ink

15

The Right Thing - Apply Good Visualization Practices

16

bull Dont use graphics to decorate a few numbersbull Reduce data ink ratiobull Visualization principles

The 2nd Dichotomy

17

bull donrsquot understand the real impactbull get the 70 solutionbull donrsquot think bigbull no timemoney for real researchbull canrsquot scalebull work based off of a few

customerrsquos input

bull donrsquot know whatrsquos been done in industrybull donrsquot understand the use-casesbull donrsquot understand the environments

data domainbull work on simulated databull construct their own problems bull use overly complicated impractical

solutionsbull use graphs visualization where it is not

needed

Some comments are based on paper reviews from RAID 200708 VizSec 200708

Industry Academia

two worldsIndustry amp Academia

The Way Forward

18

bull Building a secviz disciplinebull Bridging the gapbull Learning the ldquootherrdquo disciplinebull More academia industry collaboration

Security Visualization

SecViz

My Focus Areas

19

bull Use-case oriented visualizationbull IT data managementbull Perimeter Threatbull Governance Risk Compliance (GRC)bull Insider Threat bull IT data visualizationbull SecVizOrgbull DAVIX

IT Data Management

A Shifted Crime Landscapebull Crimes are moving up the stack

bull Insider crime

bull Large-scale spread of many small attacks

bull Are you prepared

bull Are you monitoring enough

21

Application Layer

Transport Layer

Network Layer

Link Layer

Physical Layer

Questions are not known in advance Have the data when you need it

The IT Search Company

Configurations

Change Events

Traps amp Alerts

Scripts amp Code

Logs

What Is IT Datavarlogmessagsoptlog

etcsyslogconfetchosts

1361212533122iso org dod internet mgmt mib-2 host hrDevice hrProcessorTable hrProcessorEntry hrProcessorLoad

psnetstat

File system changesWindows Registry

multi-line files

entire files

multi-line structures

multi-line table format

hooks into the OS

Perimeter Threat

Applied Security Visualization Chapter 6

Sparklines

24

bull Data-intense design-simple word-sized graphics

bull Examples- stock price over a day- access to port 80 over the last week

Edward Tufte (2006) Beautiful Evidence Graphics Press

Average Standard Deviation

bull Java Script Implementationhttpomnipotentnetjquerysparkline

Sparklines

25

Port Source IP Destination IP

Insider Threat

Applied Security Visualization Chapter 8

Three Types of Insider Threats

27

Fraud InformationLeak

Sabotage

Example - Insider Threat Visualization

bull More and other data sources than for the traditional security use-cases

bull Insiders often have legitimate access to machines and data You need to log more than the exceptions

bull Insider crimes are often executed on the application layer You need transaction data and chatty application logs

28

bull The questions are not known in advance bull Visualization provokes questions and

helps find answersbull Dynamic nature of fraud bull Problem for static algorithmsbull Bandits quickly adapt to fixed threshold-

based detection systemsbull Looking for any unusual patterns

User Activity

High ratio of failed logins

29

Color indicates failed logins

30

Security VisualizationCommunity

SecViz - Security VisualizationThis is a place to share discuss challenge and learn about

security visualization

Data Analysis and Visualization Linuxdavixsecvizorg

D

V

X

ToolsCapture

- Network tools

Argus

Snort

Wireshark

- Logging

syslog-ng

- Fetching data

wget

ftp

scp

Processing

- Shell tools

awk grep sed

- Graphic preprocessing

Afterglow

LGL

- Date enrichment

geoiplookup

whoisgwhois

Visualization

- Network Traffic

EtherApe

InetVis

tnv

- Generic

Afterglow

Treemap

Mondrian

R Project

Non-concluding list of tools

Thank You

raffy splunk com

Page 11: IT Data Visualization - Sumit 2008

The Failure - The Wrong Graph

11

The Right Thing - Adequate Graphs

12

The Failure - The Wrong Integration

13

bull Using proprietary data formatbull Provide parsers for various data formats

bull does not scalebull is probably buggy incomplete

bull Use wrong data access paradigm bull complex configuration

eg needs an SSH connection

usrsharemanman5launchdplist5ltxml version=10 encoding=UTF-8gtltDOCTYPE plist PUBLIC -Apple ComputerDTD PLIST 10EN httpwwwapplecomDTDsPropertyList-10dtdgtltplist version=10gtltdictgt ltkeygt_nameltkeygt ltdictgt ltkeygt_isColumnltkeygt ltstringgtYESltstringgt ltkeygt_isOutlineColumnltkeygt ltstringgtYESltstringgt ltkeygt_orderltkeygt ltstringgt0ltstringgt ltdictgt ltkeygtbsd_nameltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt62ltstringgt ltdictgt ltkeygtdetachable_driveltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt59ltstringgt ltdictgt ltkeygtdevice_manufacturerltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt41ltstringgt ltdictgt ltkeygtdevice_modelltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt42ltstringgt ltdictgt ltkeygtdevice_revisionltkeygt

bull Keep It Simple Stupidbull Use CSV inputbull Use files as inputbull Offload to other tools

bull parsersbull data conversions

The Right Thing - KISS

14

Using node sizessizesource=1sizetarget=200maxNodeSize=02

usrsharemanman5launchdplist5ltxml version=10 encoding=UTF-8gtltDOCTYPE plist PUBLIC -Apple ComputerDTD PLIST 10EN httpwwwapplecomDTDsPropertyList-10dtdgtltplist version=10gtltdictgt ltkeygt_nameltkeygt ltdictgt ltkeygt_isColumnltkeygt ltstringgtYESltstringgt ltkeygt_isOutlineColumnltkeygt ltstringgtYESltstringgt ltkeygt_orderltkeygt ltstringgt0ltstringgt ltdictgt ltkeygtbsd_nameltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt62ltstringgt ltdictgt ltkeygtdetachable_driveltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt59ltstringgt ltdictgt ltkeygtdevice_manufacturerltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt41ltstringgt ltdictgt ltkeygtdevice_modelltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt42ltstringgt ltdictgt ltkeygtdevice_revisionltkeygt

The Failure - Unnecessary Ink

15

The Right Thing - Apply Good Visualization Practices

16

bull Dont use graphics to decorate a few numbersbull Reduce data ink ratiobull Visualization principles

The 2nd Dichotomy

17

bull donrsquot understand the real impactbull get the 70 solutionbull donrsquot think bigbull no timemoney for real researchbull canrsquot scalebull work based off of a few

customerrsquos input

bull donrsquot know whatrsquos been done in industrybull donrsquot understand the use-casesbull donrsquot understand the environments

data domainbull work on simulated databull construct their own problems bull use overly complicated impractical

solutionsbull use graphs visualization where it is not

needed

Some comments are based on paper reviews from RAID 200708 VizSec 200708

Industry Academia

two worldsIndustry amp Academia

The Way Forward

18

bull Building a secviz disciplinebull Bridging the gapbull Learning the ldquootherrdquo disciplinebull More academia industry collaboration

Security Visualization

SecViz

My Focus Areas

19

bull Use-case oriented visualizationbull IT data managementbull Perimeter Threatbull Governance Risk Compliance (GRC)bull Insider Threat bull IT data visualizationbull SecVizOrgbull DAVIX

IT Data Management

A Shifted Crime Landscapebull Crimes are moving up the stack

bull Insider crime

bull Large-scale spread of many small attacks

bull Are you prepared

bull Are you monitoring enough

21

Application Layer

Transport Layer

Network Layer

Link Layer

Physical Layer

Questions are not known in advance Have the data when you need it

The IT Search Company

Configurations

Change Events

Traps amp Alerts

Scripts amp Code

Logs

What Is IT Datavarlogmessagsoptlog

etcsyslogconfetchosts

1361212533122iso org dod internet mgmt mib-2 host hrDevice hrProcessorTable hrProcessorEntry hrProcessorLoad

psnetstat

File system changesWindows Registry

multi-line files

entire files

multi-line structures

multi-line table format

hooks into the OS

Perimeter Threat

Applied Security Visualization Chapter 6

Sparklines

24

bull Data-intense design-simple word-sized graphics

bull Examples- stock price over a day- access to port 80 over the last week

Edward Tufte (2006) Beautiful Evidence Graphics Press

Average Standard Deviation

bull Java Script Implementationhttpomnipotentnetjquerysparkline

Sparklines

25

Port Source IP Destination IP

Insider Threat

Applied Security Visualization Chapter 8

Three Types of Insider Threats

27

Fraud InformationLeak

Sabotage

Example - Insider Threat Visualization

bull More and other data sources than for the traditional security use-cases

bull Insiders often have legitimate access to machines and data You need to log more than the exceptions

bull Insider crimes are often executed on the application layer You need transaction data and chatty application logs

28

bull The questions are not known in advance bull Visualization provokes questions and

helps find answersbull Dynamic nature of fraud bull Problem for static algorithmsbull Bandits quickly adapt to fixed threshold-

based detection systemsbull Looking for any unusual patterns

User Activity

High ratio of failed logins

29

Color indicates failed logins

30

Security VisualizationCommunity

SecViz - Security VisualizationThis is a place to share discuss challenge and learn about

security visualization

Data Analysis and Visualization Linuxdavixsecvizorg

D

V

X

ToolsCapture

- Network tools

Argus

Snort

Wireshark

- Logging

syslog-ng

- Fetching data

wget

ftp

scp

Processing

- Shell tools

awk grep sed

- Graphic preprocessing

Afterglow

LGL

- Date enrichment

geoiplookup

whoisgwhois

Visualization

- Network Traffic

EtherApe

InetVis

tnv

- Generic

Afterglow

Treemap

Mondrian

R Project

Non-concluding list of tools

Thank You

raffy splunk com

Page 12: IT Data Visualization - Sumit 2008

The Right Thing - Adequate Graphs

12

The Failure - The Wrong Integration

13

bull Using proprietary data formatbull Provide parsers for various data formats

bull does not scalebull is probably buggy incomplete

bull Use wrong data access paradigm bull complex configuration

eg needs an SSH connection

usrsharemanman5launchdplist5ltxml version=10 encoding=UTF-8gtltDOCTYPE plist PUBLIC -Apple ComputerDTD PLIST 10EN httpwwwapplecomDTDsPropertyList-10dtdgtltplist version=10gtltdictgt ltkeygt_nameltkeygt ltdictgt ltkeygt_isColumnltkeygt ltstringgtYESltstringgt ltkeygt_isOutlineColumnltkeygt ltstringgtYESltstringgt ltkeygt_orderltkeygt ltstringgt0ltstringgt ltdictgt ltkeygtbsd_nameltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt62ltstringgt ltdictgt ltkeygtdetachable_driveltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt59ltstringgt ltdictgt ltkeygtdevice_manufacturerltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt41ltstringgt ltdictgt ltkeygtdevice_modelltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt42ltstringgt ltdictgt ltkeygtdevice_revisionltkeygt

bull Keep It Simple Stupidbull Use CSV inputbull Use files as inputbull Offload to other tools

bull parsersbull data conversions

The Right Thing - KISS

14

Using node sizessizesource=1sizetarget=200maxNodeSize=02

usrsharemanman5launchdplist5ltxml version=10 encoding=UTF-8gtltDOCTYPE plist PUBLIC -Apple ComputerDTD PLIST 10EN httpwwwapplecomDTDsPropertyList-10dtdgtltplist version=10gtltdictgt ltkeygt_nameltkeygt ltdictgt ltkeygt_isColumnltkeygt ltstringgtYESltstringgt ltkeygt_isOutlineColumnltkeygt ltstringgtYESltstringgt ltkeygt_orderltkeygt ltstringgt0ltstringgt ltdictgt ltkeygtbsd_nameltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt62ltstringgt ltdictgt ltkeygtdetachable_driveltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt59ltstringgt ltdictgt ltkeygtdevice_manufacturerltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt41ltstringgt ltdictgt ltkeygtdevice_modelltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt42ltstringgt ltdictgt ltkeygtdevice_revisionltkeygt

The Failure - Unnecessary Ink

15

The Right Thing - Apply Good Visualization Practices

16

bull Dont use graphics to decorate a few numbersbull Reduce data ink ratiobull Visualization principles

The 2nd Dichotomy

17

bull donrsquot understand the real impactbull get the 70 solutionbull donrsquot think bigbull no timemoney for real researchbull canrsquot scalebull work based off of a few

customerrsquos input

bull donrsquot know whatrsquos been done in industrybull donrsquot understand the use-casesbull donrsquot understand the environments

data domainbull work on simulated databull construct their own problems bull use overly complicated impractical

solutionsbull use graphs visualization where it is not

needed

Some comments are based on paper reviews from RAID 200708 VizSec 200708

Industry Academia

two worldsIndustry amp Academia

The Way Forward

18

bull Building a secviz disciplinebull Bridging the gapbull Learning the ldquootherrdquo disciplinebull More academia industry collaboration

Security Visualization

SecViz

My Focus Areas

19

bull Use-case oriented visualizationbull IT data managementbull Perimeter Threatbull Governance Risk Compliance (GRC)bull Insider Threat bull IT data visualizationbull SecVizOrgbull DAVIX

IT Data Management

A Shifted Crime Landscapebull Crimes are moving up the stack

bull Insider crime

bull Large-scale spread of many small attacks

bull Are you prepared

bull Are you monitoring enough

21

Application Layer

Transport Layer

Network Layer

Link Layer

Physical Layer

Questions are not known in advance Have the data when you need it

The IT Search Company

Configurations

Change Events

Traps amp Alerts

Scripts amp Code

Logs

What Is IT Datavarlogmessagsoptlog

etcsyslogconfetchosts

1361212533122iso org dod internet mgmt mib-2 host hrDevice hrProcessorTable hrProcessorEntry hrProcessorLoad

psnetstat

File system changesWindows Registry

multi-line files

entire files

multi-line structures

multi-line table format

hooks into the OS

Perimeter Threat

Applied Security Visualization Chapter 6

Sparklines

24

bull Data-intense design-simple word-sized graphics

bull Examples- stock price over a day- access to port 80 over the last week

Edward Tufte (2006) Beautiful Evidence Graphics Press

Average Standard Deviation

bull Java Script Implementationhttpomnipotentnetjquerysparkline

Sparklines

25

Port Source IP Destination IP

Insider Threat

Applied Security Visualization Chapter 8

Three Types of Insider Threats

27

Fraud InformationLeak

Sabotage

Example - Insider Threat Visualization

bull More and other data sources than for the traditional security use-cases

bull Insiders often have legitimate access to machines and data You need to log more than the exceptions

bull Insider crimes are often executed on the application layer You need transaction data and chatty application logs

28

bull The questions are not known in advance bull Visualization provokes questions and

helps find answersbull Dynamic nature of fraud bull Problem for static algorithmsbull Bandits quickly adapt to fixed threshold-

based detection systemsbull Looking for any unusual patterns

User Activity

High ratio of failed logins

29

Color indicates failed logins

30

Security VisualizationCommunity

SecViz - Security VisualizationThis is a place to share discuss challenge and learn about

security visualization

Data Analysis and Visualization Linuxdavixsecvizorg

D

V

X

ToolsCapture

- Network tools

Argus

Snort

Wireshark

- Logging

syslog-ng

- Fetching data

wget

ftp

scp

Processing

- Shell tools

awk grep sed

- Graphic preprocessing

Afterglow

LGL

- Date enrichment

geoiplookup

whoisgwhois

Visualization

- Network Traffic

EtherApe

InetVis

tnv

- Generic

Afterglow

Treemap

Mondrian

R Project

Non-concluding list of tools

Thank You

raffy splunk com

Page 13: IT Data Visualization - Sumit 2008

The Failure - The Wrong Integration

13

bull Using proprietary data formatbull Provide parsers for various data formats

bull does not scalebull is probably buggy incomplete

bull Use wrong data access paradigm bull complex configuration

eg needs an SSH connection

usrsharemanman5launchdplist5ltxml version=10 encoding=UTF-8gtltDOCTYPE plist PUBLIC -Apple ComputerDTD PLIST 10EN httpwwwapplecomDTDsPropertyList-10dtdgtltplist version=10gtltdictgt ltkeygt_nameltkeygt ltdictgt ltkeygt_isColumnltkeygt ltstringgtYESltstringgt ltkeygt_isOutlineColumnltkeygt ltstringgtYESltstringgt ltkeygt_orderltkeygt ltstringgt0ltstringgt ltdictgt ltkeygtbsd_nameltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt62ltstringgt ltdictgt ltkeygtdetachable_driveltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt59ltstringgt ltdictgt ltkeygtdevice_manufacturerltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt41ltstringgt ltdictgt ltkeygtdevice_modelltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt42ltstringgt ltdictgt ltkeygtdevice_revisionltkeygt

bull Keep It Simple Stupidbull Use CSV inputbull Use files as inputbull Offload to other tools

bull parsersbull data conversions

The Right Thing - KISS

14

Using node sizessizesource=1sizetarget=200maxNodeSize=02

usrsharemanman5launchdplist5ltxml version=10 encoding=UTF-8gtltDOCTYPE plist PUBLIC -Apple ComputerDTD PLIST 10EN httpwwwapplecomDTDsPropertyList-10dtdgtltplist version=10gtltdictgt ltkeygt_nameltkeygt ltdictgt ltkeygt_isColumnltkeygt ltstringgtYESltstringgt ltkeygt_isOutlineColumnltkeygt ltstringgtYESltstringgt ltkeygt_orderltkeygt ltstringgt0ltstringgt ltdictgt ltkeygtbsd_nameltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt62ltstringgt ltdictgt ltkeygtdetachable_driveltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt59ltstringgt ltdictgt ltkeygtdevice_manufacturerltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt41ltstringgt ltdictgt ltkeygtdevice_modelltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt42ltstringgt ltdictgt ltkeygtdevice_revisionltkeygt

The Failure - Unnecessary Ink

15

The Right Thing - Apply Good Visualization Practices

16

bull Dont use graphics to decorate a few numbersbull Reduce data ink ratiobull Visualization principles

The 2nd Dichotomy

17

bull donrsquot understand the real impactbull get the 70 solutionbull donrsquot think bigbull no timemoney for real researchbull canrsquot scalebull work based off of a few

customerrsquos input

bull donrsquot know whatrsquos been done in industrybull donrsquot understand the use-casesbull donrsquot understand the environments

data domainbull work on simulated databull construct their own problems bull use overly complicated impractical

solutionsbull use graphs visualization where it is not

needed

Some comments are based on paper reviews from RAID 200708 VizSec 200708

Industry Academia

two worldsIndustry amp Academia

The Way Forward

18

bull Building a secviz disciplinebull Bridging the gapbull Learning the ldquootherrdquo disciplinebull More academia industry collaboration

Security Visualization

SecViz

My Focus Areas

19

bull Use-case oriented visualizationbull IT data managementbull Perimeter Threatbull Governance Risk Compliance (GRC)bull Insider Threat bull IT data visualizationbull SecVizOrgbull DAVIX

IT Data Management

A Shifted Crime Landscapebull Crimes are moving up the stack

bull Insider crime

bull Large-scale spread of many small attacks

bull Are you prepared

bull Are you monitoring enough

21

Application Layer

Transport Layer

Network Layer

Link Layer

Physical Layer

Questions are not known in advance Have the data when you need it

The IT Search Company

Configurations

Change Events

Traps amp Alerts

Scripts amp Code

Logs

What Is IT Datavarlogmessagsoptlog

etcsyslogconfetchosts

1361212533122iso org dod internet mgmt mib-2 host hrDevice hrProcessorTable hrProcessorEntry hrProcessorLoad

psnetstat

File system changesWindows Registry

multi-line files

entire files

multi-line structures

multi-line table format

hooks into the OS

Perimeter Threat

Applied Security Visualization Chapter 6

Sparklines

24

bull Data-intense design-simple word-sized graphics

bull Examples- stock price over a day- access to port 80 over the last week

Edward Tufte (2006) Beautiful Evidence Graphics Press

Average Standard Deviation

bull Java Script Implementationhttpomnipotentnetjquerysparkline

Sparklines

25

Port Source IP Destination IP

Insider Threat

Applied Security Visualization Chapter 8

Three Types of Insider Threats

27

Fraud InformationLeak

Sabotage

Example - Insider Threat Visualization

bull More and other data sources than for the traditional security use-cases

bull Insiders often have legitimate access to machines and data You need to log more than the exceptions

bull Insider crimes are often executed on the application layer You need transaction data and chatty application logs

28

bull The questions are not known in advance bull Visualization provokes questions and

helps find answersbull Dynamic nature of fraud bull Problem for static algorithmsbull Bandits quickly adapt to fixed threshold-

based detection systemsbull Looking for any unusual patterns

User Activity

High ratio of failed logins

29

Color indicates failed logins

30

Security VisualizationCommunity

SecViz - Security VisualizationThis is a place to share discuss challenge and learn about

security visualization

Data Analysis and Visualization Linuxdavixsecvizorg

D

V

X

ToolsCapture

- Network tools

Argus

Snort

Wireshark

- Logging

syslog-ng

- Fetching data

wget

ftp

scp

Processing

- Shell tools

awk grep sed

- Graphic preprocessing

Afterglow

LGL

- Date enrichment

geoiplookup

whoisgwhois

Visualization

- Network Traffic

EtherApe

InetVis

tnv

- Generic

Afterglow

Treemap

Mondrian

R Project

Non-concluding list of tools

Thank You

raffy splunk com

Page 14: IT Data Visualization - Sumit 2008

bull Keep It Simple Stupidbull Use CSV inputbull Use files as inputbull Offload to other tools

bull parsersbull data conversions

The Right Thing - KISS

14

Using node sizessizesource=1sizetarget=200maxNodeSize=02

usrsharemanman5launchdplist5ltxml version=10 encoding=UTF-8gtltDOCTYPE plist PUBLIC -Apple ComputerDTD PLIST 10EN httpwwwapplecomDTDsPropertyList-10dtdgtltplist version=10gtltdictgt ltkeygt_nameltkeygt ltdictgt ltkeygt_isColumnltkeygt ltstringgtYESltstringgt ltkeygt_isOutlineColumnltkeygt ltstringgtYESltstringgt ltkeygt_orderltkeygt ltstringgt0ltstringgt ltdictgt ltkeygtbsd_nameltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt62ltstringgt ltdictgt ltkeygtdetachable_driveltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt59ltstringgt ltdictgt ltkeygtdevice_manufacturerltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt41ltstringgt ltdictgt ltkeygtdevice_modelltkeygt ltdictgt ltkeygt_orderltkeygt ltstringgt42ltstringgt ltdictgt ltkeygtdevice_revisionltkeygt

The Failure - Unnecessary Ink

15

The Right Thing - Apply Good Visualization Practices

16

bull Dont use graphics to decorate a few numbersbull Reduce data ink ratiobull Visualization principles

The 2nd Dichotomy

17

bull donrsquot understand the real impactbull get the 70 solutionbull donrsquot think bigbull no timemoney for real researchbull canrsquot scalebull work based off of a few

customerrsquos input

bull donrsquot know whatrsquos been done in industrybull donrsquot understand the use-casesbull donrsquot understand the environments

data domainbull work on simulated databull construct their own problems bull use overly complicated impractical

solutionsbull use graphs visualization where it is not

needed

Some comments are based on paper reviews from RAID 200708 VizSec 200708

Industry Academia

two worldsIndustry amp Academia

The Way Forward

18

bull Building a secviz disciplinebull Bridging the gapbull Learning the ldquootherrdquo disciplinebull More academia industry collaboration

Security Visualization

SecViz

My Focus Areas

19

bull Use-case oriented visualizationbull IT data managementbull Perimeter Threatbull Governance Risk Compliance (GRC)bull Insider Threat bull IT data visualizationbull SecVizOrgbull DAVIX

IT Data Management

A Shifted Crime Landscapebull Crimes are moving up the stack

bull Insider crime

bull Large-scale spread of many small attacks

bull Are you prepared

bull Are you monitoring enough

21

Application Layer

Transport Layer

Network Layer

Link Layer

Physical Layer

Questions are not known in advance Have the data when you need it

The IT Search Company

Configurations

Change Events

Traps amp Alerts

Scripts amp Code

Logs

What Is IT Datavarlogmessagsoptlog

etcsyslogconfetchosts

1361212533122iso org dod internet mgmt mib-2 host hrDevice hrProcessorTable hrProcessorEntry hrProcessorLoad

psnetstat

File system changesWindows Registry

multi-line files

entire files

multi-line structures

multi-line table format

hooks into the OS

Perimeter Threat

Applied Security Visualization Chapter 6

Sparklines

24

bull Data-intense design-simple word-sized graphics

bull Examples- stock price over a day- access to port 80 over the last week

Edward Tufte (2006) Beautiful Evidence Graphics Press

Average Standard Deviation

bull Java Script Implementationhttpomnipotentnetjquerysparkline

Sparklines

25

Port Source IP Destination IP

Insider Threat

Applied Security Visualization Chapter 8

Three Types of Insider Threats

27

Fraud InformationLeak

Sabotage

Example - Insider Threat Visualization

bull More and other data sources than for the traditional security use-cases

bull Insiders often have legitimate access to machines and data You need to log more than the exceptions

bull Insider crimes are often executed on the application layer You need transaction data and chatty application logs

28

bull The questions are not known in advance bull Visualization provokes questions and

helps find answersbull Dynamic nature of fraud bull Problem for static algorithmsbull Bandits quickly adapt to fixed threshold-

based detection systemsbull Looking for any unusual patterns

User Activity

High ratio of failed logins

29

Color indicates failed logins

30

Security VisualizationCommunity

SecViz - Security VisualizationThis is a place to share discuss challenge and learn about

security visualization

Data Analysis and Visualization Linuxdavixsecvizorg

D

V

X

ToolsCapture

- Network tools

Argus

Snort

Wireshark

- Logging

syslog-ng

- Fetching data

wget

ftp

scp

Processing

- Shell tools

awk grep sed

- Graphic preprocessing

Afterglow

LGL

- Date enrichment

geoiplookup

whoisgwhois

Visualization

- Network Traffic

EtherApe

InetVis

tnv

- Generic

Afterglow

Treemap

Mondrian

R Project

Non-concluding list of tools

Thank You

raffy splunk com

Page 15: IT Data Visualization - Sumit 2008

The Failure - Unnecessary Ink

15

The Right Thing - Apply Good Visualization Practices

16

bull Dont use graphics to decorate a few numbersbull Reduce data ink ratiobull Visualization principles

The 2nd Dichotomy

17

bull donrsquot understand the real impactbull get the 70 solutionbull donrsquot think bigbull no timemoney for real researchbull canrsquot scalebull work based off of a few

customerrsquos input

bull donrsquot know whatrsquos been done in industrybull donrsquot understand the use-casesbull donrsquot understand the environments

data domainbull work on simulated databull construct their own problems bull use overly complicated impractical

solutionsbull use graphs visualization where it is not

needed

Some comments are based on paper reviews from RAID 200708 VizSec 200708

Industry Academia

two worldsIndustry amp Academia

The Way Forward

18

bull Building a secviz disciplinebull Bridging the gapbull Learning the ldquootherrdquo disciplinebull More academia industry collaboration

Security Visualization

SecViz

My Focus Areas

19

bull Use-case oriented visualizationbull IT data managementbull Perimeter Threatbull Governance Risk Compliance (GRC)bull Insider Threat bull IT data visualizationbull SecVizOrgbull DAVIX

IT Data Management

A Shifted Crime Landscapebull Crimes are moving up the stack

bull Insider crime

bull Large-scale spread of many small attacks

bull Are you prepared

bull Are you monitoring enough

21

Application Layer

Transport Layer

Network Layer

Link Layer

Physical Layer

Questions are not known in advance Have the data when you need it

The IT Search Company

Configurations

Change Events

Traps amp Alerts

Scripts amp Code

Logs

What Is IT Datavarlogmessagsoptlog

etcsyslogconfetchosts

1361212533122iso org dod internet mgmt mib-2 host hrDevice hrProcessorTable hrProcessorEntry hrProcessorLoad

psnetstat

File system changesWindows Registry

multi-line files

entire files

multi-line structures

multi-line table format

hooks into the OS

Perimeter Threat

Applied Security Visualization Chapter 6

Sparklines

24

bull Data-intense design-simple word-sized graphics

bull Examples- stock price over a day- access to port 80 over the last week

Edward Tufte (2006) Beautiful Evidence Graphics Press

Average Standard Deviation

bull Java Script Implementationhttpomnipotentnetjquerysparkline

Sparklines

25

Port Source IP Destination IP

Insider Threat

Applied Security Visualization Chapter 8

Three Types of Insider Threats

27

Fraud InformationLeak

Sabotage

Example - Insider Threat Visualization

bull More and other data sources than for the traditional security use-cases

bull Insiders often have legitimate access to machines and data You need to log more than the exceptions

bull Insider crimes are often executed on the application layer You need transaction data and chatty application logs

28

bull The questions are not known in advance bull Visualization provokes questions and

helps find answersbull Dynamic nature of fraud bull Problem for static algorithmsbull Bandits quickly adapt to fixed threshold-

based detection systemsbull Looking for any unusual patterns

User Activity

High ratio of failed logins

29

Color indicates failed logins

30

Security VisualizationCommunity

SecViz - Security VisualizationThis is a place to share discuss challenge and learn about

security visualization

Data Analysis and Visualization Linuxdavixsecvizorg

D

V

X

ToolsCapture

- Network tools

Argus

Snort

Wireshark

- Logging

syslog-ng

- Fetching data

wget

ftp

scp

Processing

- Shell tools

awk grep sed

- Graphic preprocessing

Afterglow

LGL

- Date enrichment

geoiplookup

whoisgwhois

Visualization

- Network Traffic

EtherApe

InetVis

tnv

- Generic

Afterglow

Treemap

Mondrian

R Project

Non-concluding list of tools

Thank You

raffy splunk com

Page 16: IT Data Visualization - Sumit 2008

The Right Thing - Apply Good Visualization Practices

16

bull Dont use graphics to decorate a few numbersbull Reduce data ink ratiobull Visualization principles

The 2nd Dichotomy

17

bull donrsquot understand the real impactbull get the 70 solutionbull donrsquot think bigbull no timemoney for real researchbull canrsquot scalebull work based off of a few

customerrsquos input

bull donrsquot know whatrsquos been done in industrybull donrsquot understand the use-casesbull donrsquot understand the environments

data domainbull work on simulated databull construct their own problems bull use overly complicated impractical

solutionsbull use graphs visualization where it is not

needed

Some comments are based on paper reviews from RAID 200708 VizSec 200708

Industry Academia

two worldsIndustry amp Academia

The Way Forward

18

bull Building a secviz disciplinebull Bridging the gapbull Learning the ldquootherrdquo disciplinebull More academia industry collaboration

Security Visualization

SecViz

My Focus Areas

19

bull Use-case oriented visualizationbull IT data managementbull Perimeter Threatbull Governance Risk Compliance (GRC)bull Insider Threat bull IT data visualizationbull SecVizOrgbull DAVIX

IT Data Management

A Shifted Crime Landscapebull Crimes are moving up the stack

bull Insider crime

bull Large-scale spread of many small attacks

bull Are you prepared

bull Are you monitoring enough

21

Application Layer

Transport Layer

Network Layer

Link Layer

Physical Layer

Questions are not known in advance Have the data when you need it

The IT Search Company

Configurations

Change Events

Traps amp Alerts

Scripts amp Code

Logs

What Is IT Datavarlogmessagsoptlog

etcsyslogconfetchosts

1361212533122iso org dod internet mgmt mib-2 host hrDevice hrProcessorTable hrProcessorEntry hrProcessorLoad

psnetstat

File system changesWindows Registry

multi-line files

entire files

multi-line structures

multi-line table format

hooks into the OS

Perimeter Threat

Applied Security Visualization Chapter 6

Sparklines

24

bull Data-intense design-simple word-sized graphics

bull Examples- stock price over a day- access to port 80 over the last week

Edward Tufte (2006) Beautiful Evidence Graphics Press

Average Standard Deviation

bull Java Script Implementationhttpomnipotentnetjquerysparkline

Sparklines

25

Port Source IP Destination IP

Insider Threat

Applied Security Visualization Chapter 8

Three Types of Insider Threats

27

Fraud InformationLeak

Sabotage

Example - Insider Threat Visualization

bull More and other data sources than for the traditional security use-cases

bull Insiders often have legitimate access to machines and data You need to log more than the exceptions

bull Insider crimes are often executed on the application layer You need transaction data and chatty application logs

28

bull The questions are not known in advance bull Visualization provokes questions and

helps find answersbull Dynamic nature of fraud bull Problem for static algorithmsbull Bandits quickly adapt to fixed threshold-

based detection systemsbull Looking for any unusual patterns

User Activity

High ratio of failed logins

29

Color indicates failed logins

30

Security VisualizationCommunity

SecViz - Security VisualizationThis is a place to share discuss challenge and learn about

security visualization

Data Analysis and Visualization Linuxdavixsecvizorg

D

V

X

ToolsCapture

- Network tools

Argus

Snort

Wireshark

- Logging

syslog-ng

- Fetching data

wget

ftp

scp

Processing

- Shell tools

awk grep sed

- Graphic preprocessing

Afterglow

LGL

- Date enrichment

geoiplookup

whoisgwhois

Visualization

- Network Traffic

EtherApe

InetVis

tnv

- Generic

Afterglow

Treemap

Mondrian

R Project

Non-concluding list of tools

Thank You

raffy splunk com

Page 17: IT Data Visualization - Sumit 2008

The 2nd Dichotomy

17

bull donrsquot understand the real impactbull get the 70 solutionbull donrsquot think bigbull no timemoney for real researchbull canrsquot scalebull work based off of a few

customerrsquos input

bull donrsquot know whatrsquos been done in industrybull donrsquot understand the use-casesbull donrsquot understand the environments

data domainbull work on simulated databull construct their own problems bull use overly complicated impractical

solutionsbull use graphs visualization where it is not

needed

Some comments are based on paper reviews from RAID 200708 VizSec 200708

Industry Academia

two worldsIndustry amp Academia

The Way Forward

18

bull Building a secviz disciplinebull Bridging the gapbull Learning the ldquootherrdquo disciplinebull More academia industry collaboration

Security Visualization

SecViz

My Focus Areas

19

bull Use-case oriented visualizationbull IT data managementbull Perimeter Threatbull Governance Risk Compliance (GRC)bull Insider Threat bull IT data visualizationbull SecVizOrgbull DAVIX

IT Data Management

A Shifted Crime Landscapebull Crimes are moving up the stack

bull Insider crime

bull Large-scale spread of many small attacks

bull Are you prepared

bull Are you monitoring enough

21

Application Layer

Transport Layer

Network Layer

Link Layer

Physical Layer

Questions are not known in advance Have the data when you need it

The IT Search Company

Configurations

Change Events

Traps amp Alerts

Scripts amp Code

Logs

What Is IT Datavarlogmessagsoptlog

etcsyslogconfetchosts

1361212533122iso org dod internet mgmt mib-2 host hrDevice hrProcessorTable hrProcessorEntry hrProcessorLoad

psnetstat

File system changesWindows Registry

multi-line files

entire files

multi-line structures

multi-line table format

hooks into the OS

Perimeter Threat

Applied Security Visualization Chapter 6

Sparklines

24

bull Data-intense design-simple word-sized graphics

bull Examples- stock price over a day- access to port 80 over the last week

Edward Tufte (2006) Beautiful Evidence Graphics Press

Average Standard Deviation

bull Java Script Implementationhttpomnipotentnetjquerysparkline

Sparklines

25

Port Source IP Destination IP

Insider Threat

Applied Security Visualization Chapter 8

Three Types of Insider Threats

27

Fraud InformationLeak

Sabotage

Example - Insider Threat Visualization

bull More and other data sources than for the traditional security use-cases

bull Insiders often have legitimate access to machines and data You need to log more than the exceptions

bull Insider crimes are often executed on the application layer You need transaction data and chatty application logs

28

bull The questions are not known in advance bull Visualization provokes questions and

helps find answersbull Dynamic nature of fraud bull Problem for static algorithmsbull Bandits quickly adapt to fixed threshold-

based detection systemsbull Looking for any unusual patterns

User Activity

High ratio of failed logins

29

Color indicates failed logins

30

Security VisualizationCommunity

SecViz - Security VisualizationThis is a place to share discuss challenge and learn about

security visualization

Data Analysis and Visualization Linuxdavixsecvizorg

D

V

X

ToolsCapture

- Network tools

Argus

Snort

Wireshark

- Logging

syslog-ng

- Fetching data

wget

ftp

scp

Processing

- Shell tools

awk grep sed

- Graphic preprocessing

Afterglow

LGL

- Date enrichment

geoiplookup

whoisgwhois

Visualization

- Network Traffic

EtherApe

InetVis

tnv

- Generic

Afterglow

Treemap

Mondrian

R Project

Non-concluding list of tools

Thank You

raffy splunk com

Page 18: IT Data Visualization - Sumit 2008

The Way Forward

18

bull Building a secviz disciplinebull Bridging the gapbull Learning the ldquootherrdquo disciplinebull More academia industry collaboration

Security Visualization

SecViz

My Focus Areas

19

bull Use-case oriented visualizationbull IT data managementbull Perimeter Threatbull Governance Risk Compliance (GRC)bull Insider Threat bull IT data visualizationbull SecVizOrgbull DAVIX

IT Data Management

A Shifted Crime Landscapebull Crimes are moving up the stack

bull Insider crime

bull Large-scale spread of many small attacks

bull Are you prepared

bull Are you monitoring enough

21

Application Layer

Transport Layer

Network Layer

Link Layer

Physical Layer

Questions are not known in advance Have the data when you need it

The IT Search Company

Configurations

Change Events

Traps amp Alerts

Scripts amp Code

Logs

What Is IT Datavarlogmessagsoptlog

etcsyslogconfetchosts

1361212533122iso org dod internet mgmt mib-2 host hrDevice hrProcessorTable hrProcessorEntry hrProcessorLoad

psnetstat

File system changesWindows Registry

multi-line files

entire files

multi-line structures

multi-line table format

hooks into the OS

Perimeter Threat

Applied Security Visualization Chapter 6

Sparklines

24

bull Data-intense design-simple word-sized graphics

bull Examples- stock price over a day- access to port 80 over the last week

Edward Tufte (2006) Beautiful Evidence Graphics Press

Average Standard Deviation

bull Java Script Implementationhttpomnipotentnetjquerysparkline

Sparklines

25

Port Source IP Destination IP

Insider Threat

Applied Security Visualization Chapter 8

Three Types of Insider Threats

27

Fraud InformationLeak

Sabotage

Example - Insider Threat Visualization

bull More and other data sources than for the traditional security use-cases

bull Insiders often have legitimate access to machines and data You need to log more than the exceptions

bull Insider crimes are often executed on the application layer You need transaction data and chatty application logs

28

bull The questions are not known in advance bull Visualization provokes questions and

helps find answersbull Dynamic nature of fraud bull Problem for static algorithmsbull Bandits quickly adapt to fixed threshold-

based detection systemsbull Looking for any unusual patterns

User Activity

High ratio of failed logins

29

Color indicates failed logins

30

Security VisualizationCommunity

SecViz - Security VisualizationThis is a place to share discuss challenge and learn about

security visualization

Data Analysis and Visualization Linuxdavixsecvizorg

D

V

X

ToolsCapture

- Network tools

Argus

Snort

Wireshark

- Logging

syslog-ng

- Fetching data

wget

ftp

scp

Processing

- Shell tools

awk grep sed

- Graphic preprocessing

Afterglow

LGL

- Date enrichment

geoiplookup

whoisgwhois

Visualization

- Network Traffic

EtherApe

InetVis

tnv

- Generic

Afterglow

Treemap

Mondrian

R Project

Non-concluding list of tools

Thank You

raffy splunk com

Page 19: IT Data Visualization - Sumit 2008

My Focus Areas

19

bull Use-case oriented visualizationbull IT data managementbull Perimeter Threatbull Governance Risk Compliance (GRC)bull Insider Threat bull IT data visualizationbull SecVizOrgbull DAVIX

IT Data Management

A Shifted Crime Landscapebull Crimes are moving up the stack

bull Insider crime

bull Large-scale spread of many small attacks

bull Are you prepared

bull Are you monitoring enough

21

Application Layer

Transport Layer

Network Layer

Link Layer

Physical Layer

Questions are not known in advance Have the data when you need it

The IT Search Company

Configurations

Change Events

Traps amp Alerts

Scripts amp Code

Logs

What Is IT Datavarlogmessagsoptlog

etcsyslogconfetchosts

1361212533122iso org dod internet mgmt mib-2 host hrDevice hrProcessorTable hrProcessorEntry hrProcessorLoad

psnetstat

File system changesWindows Registry

multi-line files

entire files

multi-line structures

multi-line table format

hooks into the OS

Perimeter Threat

Applied Security Visualization Chapter 6

Sparklines

24

bull Data-intense design-simple word-sized graphics

bull Examples- stock price over a day- access to port 80 over the last week

Edward Tufte (2006) Beautiful Evidence Graphics Press

Average Standard Deviation

bull Java Script Implementationhttpomnipotentnetjquerysparkline

Sparklines

25

Port Source IP Destination IP

Insider Threat

Applied Security Visualization Chapter 8

Three Types of Insider Threats

27

Fraud InformationLeak

Sabotage

Example - Insider Threat Visualization

bull More and other data sources than for the traditional security use-cases

bull Insiders often have legitimate access to machines and data You need to log more than the exceptions

bull Insider crimes are often executed on the application layer You need transaction data and chatty application logs

28

bull The questions are not known in advance bull Visualization provokes questions and

helps find answersbull Dynamic nature of fraud bull Problem for static algorithmsbull Bandits quickly adapt to fixed threshold-

based detection systemsbull Looking for any unusual patterns

User Activity

High ratio of failed logins

29

Color indicates failed logins

30

Security VisualizationCommunity

SecViz - Security VisualizationThis is a place to share discuss challenge and learn about

security visualization

Data Analysis and Visualization Linuxdavixsecvizorg

D

V

X

ToolsCapture

- Network tools

Argus

Snort

Wireshark

- Logging

syslog-ng

- Fetching data

wget

ftp

scp

Processing

- Shell tools

awk grep sed

- Graphic preprocessing

Afterglow

LGL

- Date enrichment

geoiplookup

whoisgwhois

Visualization

- Network Traffic

EtherApe

InetVis

tnv

- Generic

Afterglow

Treemap

Mondrian

R Project

Non-concluding list of tools

Thank You

raffy splunk com

Page 20: IT Data Visualization - Sumit 2008

IT Data Management

A Shifted Crime Landscapebull Crimes are moving up the stack

bull Insider crime

bull Large-scale spread of many small attacks

bull Are you prepared

bull Are you monitoring enough

21

Application Layer

Transport Layer

Network Layer

Link Layer

Physical Layer

Questions are not known in advance Have the data when you need it

The IT Search Company

Configurations

Change Events

Traps amp Alerts

Scripts amp Code

Logs

What Is IT Datavarlogmessagsoptlog

etcsyslogconfetchosts

1361212533122iso org dod internet mgmt mib-2 host hrDevice hrProcessorTable hrProcessorEntry hrProcessorLoad

psnetstat

File system changesWindows Registry

multi-line files

entire files

multi-line structures

multi-line table format

hooks into the OS

Perimeter Threat

Applied Security Visualization Chapter 6

Sparklines

24

bull Data-intense design-simple word-sized graphics

bull Examples- stock price over a day- access to port 80 over the last week

Edward Tufte (2006) Beautiful Evidence Graphics Press

Average Standard Deviation

bull Java Script Implementationhttpomnipotentnetjquerysparkline

Sparklines

25

Port Source IP Destination IP

Insider Threat

Applied Security Visualization Chapter 8

Three Types of Insider Threats

27

Fraud InformationLeak

Sabotage

Example - Insider Threat Visualization

bull More and other data sources than for the traditional security use-cases

bull Insiders often have legitimate access to machines and data You need to log more than the exceptions

bull Insider crimes are often executed on the application layer You need transaction data and chatty application logs

28

bull The questions are not known in advance bull Visualization provokes questions and

helps find answersbull Dynamic nature of fraud bull Problem for static algorithmsbull Bandits quickly adapt to fixed threshold-

based detection systemsbull Looking for any unusual patterns

User Activity

High ratio of failed logins

29

Color indicates failed logins

30

Security VisualizationCommunity

SecViz - Security VisualizationThis is a place to share discuss challenge and learn about

security visualization

Data Analysis and Visualization Linuxdavixsecvizorg

D

V

X

ToolsCapture

- Network tools

Argus

Snort

Wireshark

- Logging

syslog-ng

- Fetching data

wget

ftp

scp

Processing

- Shell tools

awk grep sed

- Graphic preprocessing

Afterglow

LGL

- Date enrichment

geoiplookup

whoisgwhois

Visualization

- Network Traffic

EtherApe

InetVis

tnv

- Generic

Afterglow

Treemap

Mondrian

R Project

Non-concluding list of tools

Thank You

raffy splunk com

Page 21: IT Data Visualization - Sumit 2008

A Shifted Crime Landscapebull Crimes are moving up the stack

bull Insider crime

bull Large-scale spread of many small attacks

bull Are you prepared

bull Are you monitoring enough

21

Application Layer

Transport Layer

Network Layer

Link Layer

Physical Layer

Questions are not known in advance Have the data when you need it

The IT Search Company

Configurations

Change Events

Traps amp Alerts

Scripts amp Code

Logs

What Is IT Datavarlogmessagsoptlog

etcsyslogconfetchosts

1361212533122iso org dod internet mgmt mib-2 host hrDevice hrProcessorTable hrProcessorEntry hrProcessorLoad

psnetstat

File system changesWindows Registry

multi-line files

entire files

multi-line structures

multi-line table format

hooks into the OS

Perimeter Threat

Applied Security Visualization Chapter 6

Sparklines

24

bull Data-intense design-simple word-sized graphics

bull Examples- stock price over a day- access to port 80 over the last week

Edward Tufte (2006) Beautiful Evidence Graphics Press

Average Standard Deviation

bull Java Script Implementationhttpomnipotentnetjquerysparkline

Sparklines

25

Port Source IP Destination IP

Insider Threat

Applied Security Visualization Chapter 8

Three Types of Insider Threats

27

Fraud InformationLeak

Sabotage

Example - Insider Threat Visualization

bull More and other data sources than for the traditional security use-cases

bull Insiders often have legitimate access to machines and data You need to log more than the exceptions

bull Insider crimes are often executed on the application layer You need transaction data and chatty application logs

28

bull The questions are not known in advance bull Visualization provokes questions and

helps find answersbull Dynamic nature of fraud bull Problem for static algorithmsbull Bandits quickly adapt to fixed threshold-

based detection systemsbull Looking for any unusual patterns

User Activity

High ratio of failed logins

29

Color indicates failed logins

30

Security VisualizationCommunity

SecViz - Security VisualizationThis is a place to share discuss challenge and learn about

security visualization

Data Analysis and Visualization Linuxdavixsecvizorg

D

V

X

ToolsCapture

- Network tools

Argus

Snort

Wireshark

- Logging

syslog-ng

- Fetching data

wget

ftp

scp

Processing

- Shell tools

awk grep sed

- Graphic preprocessing

Afterglow

LGL

- Date enrichment

geoiplookup

whoisgwhois

Visualization

- Network Traffic

EtherApe

InetVis

tnv

- Generic

Afterglow

Treemap

Mondrian

R Project

Non-concluding list of tools

Thank You

raffy splunk com

Page 22: IT Data Visualization - Sumit 2008

The IT Search Company

Configurations

Change Events

Traps amp Alerts

Scripts amp Code

Logs

What Is IT Datavarlogmessagsoptlog

etcsyslogconfetchosts

1361212533122iso org dod internet mgmt mib-2 host hrDevice hrProcessorTable hrProcessorEntry hrProcessorLoad

psnetstat

File system changesWindows Registry

multi-line files

entire files

multi-line structures

multi-line table format

hooks into the OS

Perimeter Threat

Applied Security Visualization Chapter 6

Sparklines

24

bull Data-intense design-simple word-sized graphics

bull Examples- stock price over a day- access to port 80 over the last week

Edward Tufte (2006) Beautiful Evidence Graphics Press

Average Standard Deviation

bull Java Script Implementationhttpomnipotentnetjquerysparkline

Sparklines

25

Port Source IP Destination IP

Insider Threat

Applied Security Visualization Chapter 8

Three Types of Insider Threats

27

Fraud InformationLeak

Sabotage

Example - Insider Threat Visualization

bull More and other data sources than for the traditional security use-cases

bull Insiders often have legitimate access to machines and data You need to log more than the exceptions

bull Insider crimes are often executed on the application layer You need transaction data and chatty application logs

28

bull The questions are not known in advance bull Visualization provokes questions and

helps find answersbull Dynamic nature of fraud bull Problem for static algorithmsbull Bandits quickly adapt to fixed threshold-

based detection systemsbull Looking for any unusual patterns

User Activity

High ratio of failed logins

29

Color indicates failed logins

30

Security VisualizationCommunity

SecViz - Security VisualizationThis is a place to share discuss challenge and learn about

security visualization

Data Analysis and Visualization Linuxdavixsecvizorg

D

V

X

ToolsCapture

- Network tools

Argus

Snort

Wireshark

- Logging

syslog-ng

- Fetching data

wget

ftp

scp

Processing

- Shell tools

awk grep sed

- Graphic preprocessing

Afterglow

LGL

- Date enrichment

geoiplookup

whoisgwhois

Visualization

- Network Traffic

EtherApe

InetVis

tnv

- Generic

Afterglow

Treemap

Mondrian

R Project

Non-concluding list of tools

Thank You

raffy splunk com

Page 23: IT Data Visualization - Sumit 2008

Perimeter Threat

Applied Security Visualization Chapter 6

Sparklines

24

bull Data-intense design-simple word-sized graphics

bull Examples- stock price over a day- access to port 80 over the last week

Edward Tufte (2006) Beautiful Evidence Graphics Press

Average Standard Deviation

bull Java Script Implementationhttpomnipotentnetjquerysparkline

Sparklines

25

Port Source IP Destination IP

Insider Threat

Applied Security Visualization Chapter 8

Three Types of Insider Threats

27

Fraud InformationLeak

Sabotage

Example - Insider Threat Visualization

bull More and other data sources than for the traditional security use-cases

bull Insiders often have legitimate access to machines and data You need to log more than the exceptions

bull Insider crimes are often executed on the application layer You need transaction data and chatty application logs

28

bull The questions are not known in advance bull Visualization provokes questions and

helps find answersbull Dynamic nature of fraud bull Problem for static algorithmsbull Bandits quickly adapt to fixed threshold-

based detection systemsbull Looking for any unusual patterns

User Activity

High ratio of failed logins

29

Color indicates failed logins

30

Security VisualizationCommunity

SecViz - Security VisualizationThis is a place to share discuss challenge and learn about

security visualization

Data Analysis and Visualization Linuxdavixsecvizorg

D

V

X

ToolsCapture

- Network tools

Argus

Snort

Wireshark

- Logging

syslog-ng

- Fetching data

wget

ftp

scp

Processing

- Shell tools

awk grep sed

- Graphic preprocessing

Afterglow

LGL

- Date enrichment

geoiplookup

whoisgwhois

Visualization

- Network Traffic

EtherApe

InetVis

tnv

- Generic

Afterglow

Treemap

Mondrian

R Project

Non-concluding list of tools

Thank You

raffy splunk com

Page 24: IT Data Visualization - Sumit 2008

Sparklines

24

bull Data-intense design-simple word-sized graphics

bull Examples- stock price over a day- access to port 80 over the last week

Edward Tufte (2006) Beautiful Evidence Graphics Press

Average Standard Deviation

bull Java Script Implementationhttpomnipotentnetjquerysparkline

Sparklines

25

Port Source IP Destination IP

Insider Threat

Applied Security Visualization Chapter 8

Three Types of Insider Threats

27

Fraud InformationLeak

Sabotage

Example - Insider Threat Visualization

bull More and other data sources than for the traditional security use-cases

bull Insiders often have legitimate access to machines and data You need to log more than the exceptions

bull Insider crimes are often executed on the application layer You need transaction data and chatty application logs

28

bull The questions are not known in advance bull Visualization provokes questions and

helps find answersbull Dynamic nature of fraud bull Problem for static algorithmsbull Bandits quickly adapt to fixed threshold-

based detection systemsbull Looking for any unusual patterns

User Activity

High ratio of failed logins

29

Color indicates failed logins

30

Security VisualizationCommunity

SecViz - Security VisualizationThis is a place to share discuss challenge and learn about

security visualization

Data Analysis and Visualization Linuxdavixsecvizorg

D

V

X

ToolsCapture

- Network tools

Argus

Snort

Wireshark

- Logging

syslog-ng

- Fetching data

wget

ftp

scp

Processing

- Shell tools

awk grep sed

- Graphic preprocessing

Afterglow

LGL

- Date enrichment

geoiplookup

whoisgwhois

Visualization

- Network Traffic

EtherApe

InetVis

tnv

- Generic

Afterglow

Treemap

Mondrian

R Project

Non-concluding list of tools

Thank You

raffy splunk com

Page 25: IT Data Visualization - Sumit 2008

Sparklines

25

Port Source IP Destination IP

Insider Threat

Applied Security Visualization Chapter 8

Three Types of Insider Threats

27

Fraud InformationLeak

Sabotage

Example - Insider Threat Visualization

bull More and other data sources than for the traditional security use-cases

bull Insiders often have legitimate access to machines and data You need to log more than the exceptions

bull Insider crimes are often executed on the application layer You need transaction data and chatty application logs

28

bull The questions are not known in advance bull Visualization provokes questions and

helps find answersbull Dynamic nature of fraud bull Problem for static algorithmsbull Bandits quickly adapt to fixed threshold-

based detection systemsbull Looking for any unusual patterns

User Activity

High ratio of failed logins

29

Color indicates failed logins

30

Security VisualizationCommunity

SecViz - Security VisualizationThis is a place to share discuss challenge and learn about

security visualization

Data Analysis and Visualization Linuxdavixsecvizorg

D

V

X

ToolsCapture

- Network tools

Argus

Snort

Wireshark

- Logging

syslog-ng

- Fetching data

wget

ftp

scp

Processing

- Shell tools

awk grep sed

- Graphic preprocessing

Afterglow

LGL

- Date enrichment

geoiplookup

whoisgwhois

Visualization

- Network Traffic

EtherApe

InetVis

tnv

- Generic

Afterglow

Treemap

Mondrian

R Project

Non-concluding list of tools

Thank You

raffy splunk com

Page 26: IT Data Visualization - Sumit 2008

Insider Threat

Applied Security Visualization Chapter 8

Three Types of Insider Threats

27

Fraud InformationLeak

Sabotage

Example - Insider Threat Visualization

bull More and other data sources than for the traditional security use-cases

bull Insiders often have legitimate access to machines and data You need to log more than the exceptions

bull Insider crimes are often executed on the application layer You need transaction data and chatty application logs

28

bull The questions are not known in advance bull Visualization provokes questions and

helps find answersbull Dynamic nature of fraud bull Problem for static algorithmsbull Bandits quickly adapt to fixed threshold-

based detection systemsbull Looking for any unusual patterns

User Activity

High ratio of failed logins

29

Color indicates failed logins

30

Security VisualizationCommunity

SecViz - Security VisualizationThis is a place to share discuss challenge and learn about

security visualization

Data Analysis and Visualization Linuxdavixsecvizorg

D

V

X

ToolsCapture

- Network tools

Argus

Snort

Wireshark

- Logging

syslog-ng

- Fetching data

wget

ftp

scp

Processing

- Shell tools

awk grep sed

- Graphic preprocessing

Afterglow

LGL

- Date enrichment

geoiplookup

whoisgwhois

Visualization

- Network Traffic

EtherApe

InetVis

tnv

- Generic

Afterglow

Treemap

Mondrian

R Project

Non-concluding list of tools

Thank You

raffy splunk com

Page 27: IT Data Visualization - Sumit 2008

Three Types of Insider Threats

27

Fraud InformationLeak

Sabotage

Example - Insider Threat Visualization

bull More and other data sources than for the traditional security use-cases

bull Insiders often have legitimate access to machines and data You need to log more than the exceptions

bull Insider crimes are often executed on the application layer You need transaction data and chatty application logs

28

bull The questions are not known in advance bull Visualization provokes questions and

helps find answersbull Dynamic nature of fraud bull Problem for static algorithmsbull Bandits quickly adapt to fixed threshold-

based detection systemsbull Looking for any unusual patterns

User Activity

High ratio of failed logins

29

Color indicates failed logins

30

Security VisualizationCommunity

SecViz - Security VisualizationThis is a place to share discuss challenge and learn about

security visualization

Data Analysis and Visualization Linuxdavixsecvizorg

D

V

X

ToolsCapture

- Network tools

Argus

Snort

Wireshark

- Logging

syslog-ng

- Fetching data

wget

ftp

scp

Processing

- Shell tools

awk grep sed

- Graphic preprocessing

Afterglow

LGL

- Date enrichment

geoiplookup

whoisgwhois

Visualization

- Network Traffic

EtherApe

InetVis

tnv

- Generic

Afterglow

Treemap

Mondrian

R Project

Non-concluding list of tools

Thank You

raffy splunk com

Page 28: IT Data Visualization - Sumit 2008

Example - Insider Threat Visualization

bull More and other data sources than for the traditional security use-cases

bull Insiders often have legitimate access to machines and data You need to log more than the exceptions

bull Insider crimes are often executed on the application layer You need transaction data and chatty application logs

28

bull The questions are not known in advance bull Visualization provokes questions and

helps find answersbull Dynamic nature of fraud bull Problem for static algorithmsbull Bandits quickly adapt to fixed threshold-

based detection systemsbull Looking for any unusual patterns

User Activity

High ratio of failed logins

29

Color indicates failed logins

30

Security VisualizationCommunity

SecViz - Security VisualizationThis is a place to share discuss challenge and learn about

security visualization

Data Analysis and Visualization Linuxdavixsecvizorg

D

V

X

ToolsCapture

- Network tools

Argus

Snort

Wireshark

- Logging

syslog-ng

- Fetching data

wget

ftp

scp

Processing

- Shell tools

awk grep sed

- Graphic preprocessing

Afterglow

LGL

- Date enrichment

geoiplookup

whoisgwhois

Visualization

- Network Traffic

EtherApe

InetVis

tnv

- Generic

Afterglow

Treemap

Mondrian

R Project

Non-concluding list of tools

Thank You

raffy splunk com

Page 29: IT Data Visualization - Sumit 2008

User Activity

High ratio of failed logins

29

Color indicates failed logins

30

Security VisualizationCommunity

SecViz - Security VisualizationThis is a place to share discuss challenge and learn about

security visualization

Data Analysis and Visualization Linuxdavixsecvizorg

D

V

X

ToolsCapture

- Network tools

Argus

Snort

Wireshark

- Logging

syslog-ng

- Fetching data

wget

ftp

scp

Processing

- Shell tools

awk grep sed

- Graphic preprocessing

Afterglow

LGL

- Date enrichment

geoiplookup

whoisgwhois

Visualization

- Network Traffic

EtherApe

InetVis

tnv

- Generic

Afterglow

Treemap

Mondrian

R Project

Non-concluding list of tools

Thank You

raffy splunk com

Page 30: IT Data Visualization - Sumit 2008

30

Security VisualizationCommunity

SecViz - Security VisualizationThis is a place to share discuss challenge and learn about

security visualization

Data Analysis and Visualization Linuxdavixsecvizorg

D

V

X

ToolsCapture

- Network tools

Argus

Snort

Wireshark

- Logging

syslog-ng

- Fetching data

wget

ftp

scp

Processing

- Shell tools

awk grep sed

- Graphic preprocessing

Afterglow

LGL

- Date enrichment

geoiplookup

whoisgwhois

Visualization

- Network Traffic

EtherApe

InetVis

tnv

- Generic

Afterglow

Treemap

Mondrian

R Project

Non-concluding list of tools

Thank You

raffy splunk com

Page 31: IT Data Visualization - Sumit 2008

Security VisualizationCommunity

SecViz - Security VisualizationThis is a place to share discuss challenge and learn about

security visualization

Data Analysis and Visualization Linuxdavixsecvizorg

D

V

X

ToolsCapture

- Network tools

Argus

Snort

Wireshark

- Logging

syslog-ng

- Fetching data

wget

ftp

scp

Processing

- Shell tools

awk grep sed

- Graphic preprocessing

Afterglow

LGL

- Date enrichment

geoiplookup

whoisgwhois

Visualization

- Network Traffic

EtherApe

InetVis

tnv

- Generic

Afterglow

Treemap

Mondrian

R Project

Non-concluding list of tools

Thank You

raffy splunk com

Page 32: IT Data Visualization - Sumit 2008

SecViz - Security VisualizationThis is a place to share discuss challenge and learn about

security visualization

Data Analysis and Visualization Linuxdavixsecvizorg

D

V

X

ToolsCapture

- Network tools

Argus

Snort

Wireshark

- Logging

syslog-ng

- Fetching data

wget

ftp

scp

Processing

- Shell tools

awk grep sed

- Graphic preprocessing

Afterglow

LGL

- Date enrichment

geoiplookup

whoisgwhois

Visualization

- Network Traffic

EtherApe

InetVis

tnv

- Generic

Afterglow

Treemap

Mondrian

R Project

Non-concluding list of tools

Thank You

raffy splunk com

Page 33: IT Data Visualization - Sumit 2008

Data Analysis and Visualization Linuxdavixsecvizorg

D

V

X

ToolsCapture

- Network tools

Argus

Snort

Wireshark

- Logging

syslog-ng

- Fetching data

wget

ftp

scp

Processing

- Shell tools

awk grep sed

- Graphic preprocessing

Afterglow

LGL

- Date enrichment

geoiplookup

whoisgwhois

Visualization

- Network Traffic

EtherApe

InetVis

tnv

- Generic

Afterglow

Treemap

Mondrian

R Project

Non-concluding list of tools

Thank You

raffy splunk com

Page 34: IT Data Visualization - Sumit 2008

ToolsCapture

- Network tools

Argus

Snort

Wireshark

- Logging

syslog-ng

- Fetching data

wget

ftp

scp

Processing

- Shell tools

awk grep sed

- Graphic preprocessing

Afterglow

LGL

- Date enrichment

geoiplookup

whoisgwhois

Visualization

- Network Traffic

EtherApe

InetVis

tnv

- Generic

Afterglow

Treemap

Mondrian

R Project

Non-concluding list of tools

Thank You

raffy splunk com

Page 35: IT Data Visualization - Sumit 2008

Thank You

raffy splunk com