Improving Spam Blacklisting Through Dynamic Thresholding and Speculative Aggregation

Proceedings of the Symposium on Network and Distributed System Security, NDSS 2010

Volumn , Issue , 2010, Pages

  Sinha, Sushant ^a   Bailey, Michael ^a   Jahanian, Farnam ^a  

^a UNIVERSITY OF MICHIGAN   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ELECTRONIC MAIL;

'CURRENT; DYNAMIC POLICY ENFORCEMENT; DYNAMIC THRESHOLDING; E-MAIL CONNECTION; E-MAIL SYSTEMS; FALSE POSITIVE; FALSE POSITIVE AND FALSE NEGATIVES; REAL- TIME; SYSTEM DESIGNERS; UNSOLICITED BULK E-MAIL;

NETWORK SECURITY;

EID: 84968706754     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (31)

1. Not just another bogus list. http://njabl.org.
2. Pyzor. http://pyzor.sourceforge.net/.
3. Sorbs DNSBL. http://www.sorbs.net.
4. SpamCop.net - Beware of cheap imitations. http://www.spamcop.net/.
5. The SpamHaus Project. http://www.spamhaus.org.
6. Vipul’s razor. http://razor.sourceforge.net/.
7. What is the SpamCop Blocking List (SCBL)? http://spamcop.net/fom-serve/cache/297.html.
8. R. Beverly and K. Sollins. Exploiting transport-level characteristics of spam. In Proceedings of the Fifth Conference on Email and Anti-Spam (CEAS), Aug. 2008.
9. D. Dittrich, M. D. Bailey, and S. Dietrich. Towards community standards for ethical behavior in computer security research. Technical Report 2009-01, Stevens Institute of Technology, Hoboken, NJ, USA, April 2009.
10. H. Drucker, V. Vapnik, and D. Wu. Support vector machines for spam categorization. IEEE Transactions on Neural Networks, 10(5):1048–1054, 1999.
11. S. Hao, N. A. Syed, N. Feamster, A. Gray, and S. Krasser. Detecting Spammers with SNARE: Spatiotemporal Network-level Automatic Reputation Engine. In Usenix Security’09, Montreal, Canada, August 2009.
12. C. Kanich, C. Kreibich, K. Levchenko, B. Enright, G. M. Voelker, V. Paxson, and S. Savage. Spamalytics: an empirical analysis of spam marketing conversion. In CCS’08: Proceedings of the 15th ACM conference on Computer and communications security, pages 3–14, New York, NY, USA, 2008. ACM.
13. J. Mason. Filtering Spam with SpamAssassin. SAGE-IE meeting presentation, 2002.
14. McAfee and I. International. The carbon footprint of email spam report. http://newsroom.mcafee.com/images/10039/carbonfootprint2009.pdf, April 2009.
15. T. Micro. Most abused infection vector. http://blog.trendmicro.com/most-abused-infection-vector/, December 2008.
16. B. Nelson, M. Barreno, F. J. Chi, A. D. Joseph, B. I. P. Rubinstein, U. Saini, C. Sutton, J. D. Tygar, and K. Xia. Exploiting machine learning to subvert your spam filter. In First USENIX Workshop on Large-Scale Exploits and Emergent Threats, April 2008.
17. M. A. Rajab, J. Zarfoss, F. Monrose, and A. Terzis. A multifaceted approach to understanding the botnet phenomenon. In IMC’06: Proceedings of the 6th ACM SIGCOMM on Internet measurement, pages 41–52, New York, NY, USA, 2006. ACM Press.
18. A. Ramachandran, D. Dagon, and N. Feamster. Can DNS-Based Blacklists Keep Up with Bots? . In Proceedings of the Third Conference on Email and Anti-Spam (CEAS 2006), July 2006.
19. A. Ramachandran and N. Feamster. Understanding the network-level behavior of spammers. In SIGCOMM’06: Conference on Applications, technologies, architectures, and protocols for computer communications, pages 291–302, New York, NY, USA, 2006. ACM Press.
20. A. Ramachandran, N. Feamster, and S. Vempala. Filtering spam with behavioral blacklisting. In CCS’07: Proceedings of the 14th ACM conference on Computer and communications security, pages 342–351, New York, NY, USA, 2007. ACM.
21. N. Research and KnowledgeStorm. Nucleus research: Spam costing us businesses $712 per employee each year. http://nucleusresearch.com/news/press-releases/ nucleus-research-spam-costing-us-businesses-712-per-employee-each-year/, April 2007.
22. M. Sahami, S. Dumais, D. Heckerman, and E. Horvitz. A bayesian approach to filtering junk e-mail. In AAAI-98 Workshop on Learning for Text Categorization, pages 55–62, 1998.
23. S. Sinha, M. Bailey, and F. Jahanian. Shades Of Grey: On the effectiveness of reputation based blacklists. In International Conference on Malicious and Unwanted Software (Malware 2008), October 2008.
24. B. Stone. Spam back to 94% of all e-mail, March 2009. http://bits.blogs.nytimes.com/2009/03/31/spam-back-to-94-of-all-e-mail/.
25. Unspam Technologies. Project Honey Pot. http://projecthoneypot.org, 2008.
26. S. Venkataraman, S. Sen, O. Spatscheck, P. Haffner, and D. Song. Exploiting network structure for proactive spam mitigation. In Proceedings of 16th USENIX Security Symposium, pages 1–18, Berkeley, CA, USA, 2007. USENIX Association.
27. G. L. Wittel and S. F. Wu. On attacking statistical spam filters. In Proceedings of the First Conference on Email and Anti-Spam (CEAS), 2004. Available: http://www.ceas.cc/papers-2004/170.pdf.
28. R. Wojtczuk. libnids, June 2004.
29. Y. Xie, F. Yu, K. Achan, E. Gillum, M. Goldszmidt, and T. Wobber. How dynamic are IP addresses? In SIGCOMM ’07: Conference on Applications, technologies, architectures, and protocols for computer communications, pages 301–312, New York, USA, 2007.
30. Y. Xie, F. Yu, K. Achan, R. Panigrahy, G. Hulten, and I. Osipkov. Spamming botnets: signatures and characteristics. SIGCOMM Comput. Commun. Rev., 38(4):171–182, 2008.
31. J. Zhang, P. Porras, and J. Ullrich. Highly predictive blacklisting. In 17th USENIX Security Symposium (USENIX Security’08), July-August 2008.