1. An Effective Defense Against Email Spam Laundering Paper by: Mengjun Xie, Heng Yin, Haining Wang Presented at:CCS'06 Presentation by: Devendra Salvi

2. Overview Introduction Spam Laundering Anti spam techniques Proxy based spam behavior DBSpam Evaluation Review

3. Introduction Presently spam makes 80% of emails Spam has evolved in parallel with anti spam techniques. Spammers hide using, proxies and compromised computers

4. Introduction (contd.) Detecting spam at its source by monitoring bidirectional traffic of a network DBSpam uses “packet symmetry” to break spam laundering in a network

5. Spam Laundering Spam Proxy

6. Anti Spam Techniques Existing “Anti spam techniques” are classified into, 1. “Recipient Oriented” 2. “Sender Oriented” 3. “HoneySpam”

7. Anti Spam Techniques (contd.) Recipient Oriented anti-spam techniques functions  They block / delay email spam from reaching recipients mailbox Or  Remove / mark spam in recipients mailbox

8. Anti Spam Techniques (contd.) Recipient Oriented anti-spam techniques are further classified as  Content based Email address filters Heuristic filters Machine learning based filters  Non content based

9. Anti Spam Techniques (contd.) Recipient Oriented anti-spam techniques are further classified as  Content based  Non content based DNSBL MARID Challenge response Tempfailing Delaying Sender behavior analysis

10. Anti Spam Techniques (contd.) Sender Oriented Techniques  Usage Regulations  E.g. blocking port 25, SMTP authentication  Cost based approaches  Charge the sender (postage)

11. Anti Spam Techniques (contd.) HoneySpam  It is a honeypot framework based on honeyD It deters “email address harvesters”, poison spam address databases and blocks spam that goes through the open relay / proxy decoys set by HoneySpam

12. Proxy based spam behavior Laundry path of Proxy Spamming

13. Proxy based spam behavior (contd.) Connection Correlation  There is one-to-one mapping between the upstream and downstream connections along the spam laundry path  This kind of connection is a common for proxy based spamming  In normal email delivery there is only one connection; between sender and receiving MTA

14. Proxy based spam behavior (contd.) Connection Correlation  The detection of such spam-proxy-related connection correlation is difficult because Spammers may use encryption for content It sits at network vantage points and may induce unaffordable overhead

15. Proxy based spam behavior (contd.) Spam laundering for single and multiple proxies

16. Proxy based spam behavior (contd.) Message symmetry at application layer leads to packet symmetry at network layer  Exception: one to one mapping between inbound and outbound streams can be violated Reasons: packet fragmentation, packet compression and packet retransmission

17. Proxy based spam behavior (contd.) The packet symmetry is a key to distinguish the suspicious upstream / downstream connections along the spam laundry path from normal background traffic

18. DBSpam Goals  Fast detection of spam laundering with high accuracy  Breaking spam laundering via throttling or blocking after detection  Support for spammer tracking  Support for spam message fingerprinting

19. DBSpam DBSpam consists of two major components  Spam detection module Simple connection correlation detection algorithm  Spam suppression module

20. DBSpam Deployment of DBSpam  It is placed at a network vantage point which may connect costumer network to the Internet  DBSpam works well if it is deployed at the primary ISP edge router

21. DBSpam Packet symmetry for spam TCP is 1 For a normal TCP connection it is one with very small probability of occurrence DBSpam uses a statistical method, “sequential probability ratio test” (SPRT)

22. DBSpam “sequential probability ratio test” (SPRT) checks probability between bounds for each observation The algorithm contains a variable X which is checked for correlation Variables A and B form the bounds If X is between A and B, the algorithm does another iteration, else it stops with a conclusion

23. DBSpam

24. Evaluation How fast DBSpam can detect spam laundering ? How accurate the detection results were ? How many system resources it consumes ?

25. Evaluation DBSpam detection time is mainly decided by the SPRT detection time  Number of observations needed to reach a decision  Actual time spent by SPRT

26. Evaluation SPRT can filter out 95% non-spam traffic in four observations

27. Evaluation The actual detection time is approximately 6 reply rounds of SMTP connection

28. Evaluation Accuracy The probability is less than 0.0002 in all traces, indicating that false positive probability of SPRT is fairly small False negatives are calculated using ratio of number of packets missed to number of spam packets missed

29. Evaluation Resource Consumption  Trace Information  Resource consumption

30. Review Strengths  Can detect spam sources by isolating and tracking proxies  Truncates spam at near its source  Can detect spam even if its content is encrypted  Low false positives  Does not degrade network performance

31. Review Weaknesses  It cannot efficiently detect spam with short reply rounds  Its it more effective if it can be installed on an ISP edge router  The paper does not discuss about spam suppression techniques

32. Review Improvements:  With evolving spam, DBSpam will have to tweak its spam detection algorithm

33. Questions ?