Database Techniques for fighting SPAM

Telvis Calhoun

CSc 8710 – Advanced Databases

Dr. Yingshu Li

Everybody knows about SPAM

Spam is unsolicited bulk email sent for profit and general mayhem.

BOTNETs = Distributed Network of hijacked IPs.

IPs hard to track 70 billion emails sent per day. 70%

spam

How Anti-SPAM uses DBs?

Spam databases collect network layer and application layer data.

IP Blacklisting Detect a malicious host during SMTP dialog. Difficult to detect IP address DHCP, botnet size or good

IPs used to forward Content Analysis

Detect malicious mail content. Requires that MTA complete the SMTP connection. Arms race between content filter designers and

spammers.

Summary of DB Techniques

Grey Space Analysis Trinity: Peer-to-Peer Database Behavioral Blacklisting Progressive Email Scanning Content filtering using Bayesian

Analysis

Grey Space Analysis

Characterize IP Space: Active vs. Grey Space IP Flow Database Detect malicious IPs by extracting dominant

scanning ports (DSPs) Find DSPs using relative uncertainty algorithm

Mining Technique: Relative Uncertainty

Determines entropy of IP ports in flows database. Formula := Entropy of dstPrt distribution ÷ maximum

entropy. p := number of flows with port[i] ÷ total flows RU close to 1 shows ~even distribution, near 0 shows

uneven distribution

Grey Space Algorithm

Isolate flows toward grey space

Find dominant scanning ports (DSPs)

Find outside hosts with DSPs flows toward grey and active hosts.

Find inside host footprint for outside hosts.

Classify adversary as hitter or scanner.

Focused Hitters vs Bad Scanners

Focused hitters tend to send tens or hundreds of flows to each grey host.

Bad scanners send one or a few flows to each grey host

Trinity: Distribute IP Reputation Database

Botnets send a large amount of data in a short amount of time.

Trinity uses distributed in-memory hash table containing IP reputation entries.

Each peer has 10 to 50 megabytes of data (833K – 4.17M entries)

Chord Distributed Hash Table

Distribute data over a large P2P network Quickly find any given item

Stores key/value pairs The key value controls which node(s) stores the

value Each node is responsible for some section of the

space Basic operations

Store(key; val) val = Retrieve(key)

Chord (cont)

Each node chooses a n-bit ID IDs are arranged in a ring

Each lookup key is also a n-bit ID i.e., the hash of the real lookup key Node IDs and keys occupy the same space!

Each node is responsible for storing keys “near" its ID Replication usaully between current and previous node Items can be replicated at multiple successors No single host contains large fraction of a particular space

to guard against DDoS.

Database Updates

Compute the number of interval quarters since last update. Shift and update counters accordingly

Determine site responsible for entry and send UDP. Once received by owner site, forward entry to k peers using TCP.

Updates communicative, order doesn’t matter. Consistency not required.

Even if host goes down, database can be rebuilt in an hour.

Security

Secure communications for neighbors Limit updates for nodes that have sent

more than 100 emails in 10 minutes. Falsified source IPs can cause false

positives.

Clustering Technique for Behavioral Blacklisting

Identify spammers that attack many domains.

Domain distribution and frequency is the sending pattern

Form clusters of sending patterns

Use clusters to ID new attack

Spectral Clustering

Divide Phase – produces a tree whose leaves are elements of the set.

Merge Phase – Start with each leaf in its own cluster and merge going up the tree.

Vector Generation

Database contains: M(i,j,k) Total times that IP ‘i' sent email to domain

‘j’ in time slot ‘k’. Find total flows for IP/Domain across

entire time axis (M’). Generate feature vector from M’

IP := <#flows to domain 1, # flows to domain 2, … #flows to domain j>

Clustering

Clusters contain IP addresses that send mail to similar sets of domains.

Define traffic pattern for each cluster Averaging the rows

(vector contents) for all IPs in the cluster.

IPxIP matrix of related spam senders

Classification

Input IP vector ‘r’ :=1 x d vector Use similarity algorithm to find closes cluster Spam score is the maximum similarity of r

with any cluster.

Progressive Email Scanner

Maintains Feature Instance (FI) database

FI is any feature that can discriminate HAM from SPAM.

Dynamic Features - Use any feature that IDs mail such as contents, network, etc.) Paper only uses URL links as FIs

PEC Architecture

FI States Grey (Ambiguous FI) Black (Spam FI) White (HAM FI)

Blacklist Module – Extracts and hashes FIs

Scoreboard Module – Tracks FI occurrences and timestamp (age)

Competitive Aging and Scoring System (CASS)

Transition between states governed by Score – number of occurrence of FI Age – time since last score update.

Score (R) exceeds score threshold (S) causes Grey to Black transition.

Age (A) exceeds age threshold (M) triggers Grey to White transition. Purge

Bayesian Content Filtering

Determine the probability that a message is spam based on contents

Use Bayesian combination of spam probabilities

Bayesian Training

Requires training corpus of HAM/SPAM

Find interesting tokens.

Create HAM/SPAM token tables

Classification

Tokenize new message Calculate spam probability for each message Derive overall spam probablity using Bayes formula.

Sample Message = 0.0 Non-spam tokens outweigh spam tokens to prevent

false positives

Hi,

Just a reminder: don’t forget your allergy prescription when you visit New

York City today.

MomSample Message Spam Probability Table

Real World Applications

TrustedSource.org

Messaging Security Architecture

Summary

A variety of database techniques are used in Anti-Spam Technology IP Blacklisting Content filtering

Databases can contain: Network traffic: IP Addresses, Domain, Ports Message Content: Words, URLs, HTML Text

Challenges: Scalability – Must handle many connections or messages Minimize False Positive Rates – Cannot classify a HAM

message as SPAM. Finding useful SPAM features. Using machine learning

techniques.

References

Brodsky, et al, A Distributed Content Independent Method for Spam Detection, HotBots 2007

Jin, et al, Identifying and Tracking Suspicious Activities through IP Gray Space Analysis, MineNet 2007

Liu, et al, High-Speed Detection of Unsolicited Bulk Emails, ANCS 2007

Ramachandran, A., Filtering Spam with Behavioral Blacklisting, CCS 2007

Cheng, et al., A Divide-and-Merge Methodology for Clustering, ACM Transactions on Database Systems, 2006

Graham P., A Plan for Spam, www.paulgraham.com/spam.html, 2002

Secure Computing Corporation, http://trustedsource.org, 2008