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Spamming Botnets: Signatures and Characteristics

Spamming Botnets: Signatures and Characteristics. Authors:Yinglian Xie, Fang Yu, Kannan Achan, Rina Panigrahy, Geoff Hulten+, Ivan Osipkov+. Presenter: Chia-Li Lin. References.

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Spamming Botnets: Signatures and Characteristics

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  1. Spamming Botnets: Signatures and Characteristics Authors:Yinglian Xie, Fang Yu, Kannan Achan, Rina Panigrahy, Geoff Hulten+, Ivan Osipkov+ Presenter: Chia-Li Lin

  2. References • Y. Xie, F. Yu, K. Achan, R. Panigrahy, G. Hulten, and I. Osipkov. Spamming botnets: Signatures and characteristics. In SIGCOMM, 2008

  3. Outline • Introduction • Spam Activity Trends • AutoRE Structure • Study Results • Conclusion

  4. Introduction • Developed a spam signature generation framework called: • AutoRE • To detect botnet-based spam emails and botnet membership • It outputs high quality regular expression signatures

  5. Contribution • Ability to detect frequent domain modifications • In-depth analysis of identified spamming botnet characteristics and their activity trends

  6. Two Observations • First, spammers often add random, legitimate URLs to content • legitimate and very general (e.g.,http://www.w3.org) • Second, customize polymorphic URLs

  7. Multi-URL spam emails

  8. Polymorphic URLs

  9. AutoRE • Automatically generating URL signatures to identify botnet-based spam campaigns • Produces two outputs: • a set of spam URL signatures • complete URL string (CU) • URL regular Expression (RE) • a related list of botnet host IP addresses

  10. Three modules • AutoRE is comprised of the following three modules • URL preprocessor • Group selector • RegEx generator • domain-specific • domain-agnostic

  11. AutoRE Structure[1/2]

  12. AutoRE Structure[2/2]

  13. Suffix-array algorithm

  14. keyword-based signature tree

  15. Detailing and Generalization • Detailing • returns a domain specific regular expression using a keyword-based signature as input. • Generalization • returns a more general domain-agnostic regular expression by merging very similar domain-specific regular expressions

  16. Generalization

  17. Detect Results • Using three months of sampled emails from Hotmail • November 2006, June 2007, July 2007 • AutoRE successfully detected • 7,721 spam campaigns • 340,050 distinct botnet host IP addresses • spanning 5,916 ASes.

  18. CU& RE Statistics

  19. False positive rate

  20. Conclutions • This is the first successful attempt to automatically generate regular expression signatures • The existence of botnet spam signatures and the feasibility of detecting botnet hosts using them

  21. Questions

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