BIG DATA APPROACHES TO CLOUD SECURITYPaul Morse – President, WebMall Ventures

Cloud Security Alliance, Seattle Chapter 3/28/2013

“BIG DATA IS NOT JUST ABOUT LOTS OF DATA, IT IS ABOUT HAVING THE ABILITY TO EXTRACT MEANING; TO SORT

THROUGH THE MASSES OF DATA ELEMENTS TO DISCOVER THE HIDDEN PATTERN, THE UNEXPECTED CORRELATION,”

Art Coviello, executive chairman of RSA

ON THE SURFACE, BIG DATA SEEMS TO BE ALL ABOUT BUSINESS INTELLIGENCE AND ANALYTICS, BUT IT ALSO AFFECTS THE NITTY-

GRITTY OF POWER AND COOLING, NETWORKING, STORAGE AND DATA CENTER EXPANSION.

AGENDA

• Observations

• Cloud Architectures/Components

• Machine-Generated Data• Sources of Data

• Time Sequencing of Events

• Searching for Behavior

• Recent Hack Examples

OBSERVATIONS

• Big Data solutions are changing the game for security practitioners and execs

• Provide the ability to look at discovery, detection and remediation across large portions of the organization in entirely new ways

• Correlation between seemingly unrelated events in near real time is now relatively easy

• Growing range of solution types – simple to highly complex • Roll your own to pre-packaged solutions• On-prem, Public Cloud-based and Hybrid• Simple Log search to Predictive Analysis with complex dashboards and reporting

• Some solutions have extremely short “time to value” propositions

• “Big Data Washing” like “Cloud Washing” is showing up

• Prices vary – Free to mondo

• It is NOT the holy grail for security but has many advantages over traditional SIEM products – real time, large amounts of data, broad event correlation, etc.

SET THE STAGE

• Many perspectives to Cloud Computing

• Main focus for this talk is as a Public Cloud Provider

• You are the “owner” of the facility – all of it.

• Infrastructure-centric discussion

• How do Big Data solutions improve Security?

YOUR CLOUD DATACENTER

DATA SOURCES

Routers/Switches

Servers

StoragePower

Distribution

Card Key

Systems

Door

Sensors

Water SystemTemp

Sensors

This is your attack surface

Lighting controls

Wireless Devices

SCADA

RFIDBackup Batteries

Backup Generators

PC’s

Printers

Tablets

Phones?

I want all the data in one searchable repository and available in near real time

SECURE? THINK AGAIN.

• Internet Mapping Project

• “harmless” Port ping and bot install

• 660 million IPs with 71 billion ports tested

• 460 Million Devices Responded

• Resulted in 420 thousand bots

• Stupid uid/pwd combos

• Admin/admin, Admin/no pwd, root/root, root/no pwd

• What’s on your network?

http://internetcensus2012.bitbucket.org/paper.html

CAUSE FOR PAUSE

“ We hope other researchers will find the data we have collected useful and that this publication will help raise some awareness that, while everybody is talking about high class exploits and cyberwar, four simple stupid default telnet passwords can give you access to hundreds of thousands of consumer as well as tens of thousands of industrial devices all over the world.”

MACHINE DATA

• Isn’t it really all machine data?

• Machine-generated data (MGD) is the generic term for information which was automatically created from a computer process, application, or other machine without the intervention of a human.

• Network Device Log files

• Event logs

• Application logs

• RFID logs

• Storage logs

• HVAC Logs

• Sensor data

• Etc.

MACHINE DATA EXAMPLESApache[Fri Sep 09 10:42:29.902022 2011] [core:error] [pid 35708:tid 4328636416] [client 72.15.99.187] File does not exist: /usr/local/apache2/htdocs/favicon.ico

JuniperSep 10 07:06:45 host rpd[6451]: bgp_listen_accept: Connection attempt from unconfigured neighbor: 10.0.8.1+1350

Sep 10 07:07:53 host login: 2 LOGIN FAILURES FROM 172.24.16.21

Sep 10 07:08:25 host inetd[2785]: /usr/libexec/telnetd[7251]: exit status 0x100

Oracle/Siebel

SQLParseAndExecute Statement 4 0 2003-05-13 14:07:38 select ROW_ID, NEXT_SESSION, MODIFICATION_NUM from dbo.S_SSA_ID

IIS

192.168.114.201, -, 03/20/01, 7:55:20, W3SVC2, SALES1, 172.21.13.45, 4502, 163, 3223, 200, 0, GET, /DeptLogo.gif, -,

172.16.255.255, anonymous, 03/20/01, 23:58:11, MSFTPSVC, SALES1, 172.16.255.255, 60, 275, 0, 0, 0, PASS, /Intro.htm, -,

Card Reader

10/23/04 06:16:32,Administrator,00000101,Anderman,Penny,00026,01000,10/22/2005

10/23/04 06:16:32,West Gate,00000100,Peterson,Bob,00954,01000,10/21/2005

TIME SEQUENCE OF EVENTSOutbound Traffic

Terminate Sess

Delete logs

Installer runs

Upload Small File

Command

Fail

Pass

Login Attempt

Server

TOR

LB

Front end

IP Address/Packet T0 T1 T2 T3 T4 T5 T6 T7 T8 T9 T10 T11 T12 T13 T14 T15 T16 T17 T18 T19

TIME SEQUENCE OF EVENTSTerminate Sess

Delete logs

Update

Upload Small File

Command

Fail

Pass

Login Attempt

Device

TOR

LB

Front end

IP Address/Packet T0 T1 T2 T3 T4 T5 T6 T7 T8 T9 T10 T11 T12 T13 T14 T15 T16 T17 T18

TIME SEQUENCE OF EVENTSTerminate Sess

Delete logs

Update

Upload Small File

Command

Fail

Pass

Login Attempt

Device

IP Address/Packet T0 T1 T2 T3 T4 T5 T6 T7 T8 T9 T10 T11 T12 T13 T14 T15 T16 T17 T18

Door 5

Door 4

Door 3

Door 2

Door 1

T-30 T-15 T0 T15 T30 T45

SOME AREAS TO CONSIDER• Ingesting various data formats

• Many vendors claim it is easy, when it may not be• Transforms and connectors may be required (affect performance)• Device companies create add-ons, connectors, dashboards, transforms, queries, etc• Speed of indexing determines “real time” abilities• Do you need to index ALL machine data?

• Vendor-specific Query languages• No standard, some commonality• Learning curve for seriously complex queries and operationalizing environment

• Dashboards and Visualizations Vary

• Large number of simultaneous queries is required

• Workflow is critical – what happens when you find something?

• Implementation architecture – lots of hardware? Bandwidth? Security? Users?

• Data Governance – You found what?

HACK EXAMPLES

• DOJ in January

• Defacement

• What specific behavior happened and what did they do?

• Log in Remotely

• Completely replace Index.*

• Solution – monitor index.* and set up a parsing stream and search for a code in the html. Call a workflow if the file changes or the code doesn’t match.

• DDoS

• Overwhelm Website

• Solution – compare request rate of increase to a previous ‘norm”. If the disparity is great enough, call a workflow to check IP addresses of source(s). Depending on results, do nothing or script a filter or block.

VENDORS AND GETTING STARTED

• Hadoop with Flume

• HP ArcSight

• Loggly

• Logrythm

• SumoLogic

• LogScape

• LogStash

• Sawmill

• Splunk

• Splunk Storm

• Getting Started

• Easiest – Cloud Based• Sumo Logic

• Splunk Storm

• Download and Install• Loggly

• Logrythm

• LogScape

• LogStash

• Sawmill

• Splunk

• Hadoop/Flume/Pig