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Kindred domains: detecting and clustering botnet domains using DNS traffic

Published:07 April 2014Publication History

ABSTRACT

In this paper we focus on detecting and clustering distinct groupings of domain names that are queried by numerous sets of infected machines. We propose to analyze domain name system (DNS) traffic, such as Non-Existent Domain (NXDomain) queries, at several premier Top Level Domain (TLD) authoritative name servers to identify strongly connected cliques of malware related domains. We illustrate typical malware DNS lookup patterns when observed on a global scale and utilize this insight to engineer a system capable of detecting and accurately clustering malware domains to a particular variant or malware family without the need for obtaining a malware sample. Finally, the experimental results of our system will provide a unique perspective on the current state of globally distributed malware, particularly the ones that use DNS.

References

  1. --. The conficker working group. http://bit.ly/1kAYsJA, Nov 2012.Google ScholarGoogle Scholar
  2. M. Andrews. Negative caching of DNS queries (DNS NCACHE). RFC 2308, 1998. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. M. Antonakakis, D. Dagon, X. Luo, R. Perdisci, W. Lee, and J. Bellmor. A centralized monitoring infrastructure for improving dns security. In RAID, pages 18--37, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. M. Antonakakis, R. Perdisci, D. Dagon, W. Lee, and N. Feamster. Building a dynamic reputation system for dns. In USENIX Security Symposium, pages 273--290, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. M. Antonakakis, R. Perdisci, W. Lee, N. Vasiloglou II, and D. Dagon. Detecting malware domains at the upper dns hierarchy. In USENIX Security Symposium, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. M. Antonakakis, R. Perdisci, Y. Nadji, N. Vasiloglou, S. Abu-Nimeh, W. Lee, and D. Dagon. From throw-away traffic to bots: Detecting the rise of dga-based malware. In USENIX Security, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. L. Bilge, E. Kirda, C. Kruegel, and M. Balduzzi. Exposure: Finding malicious domains using passive dns analysis. In NDSS. The Internet Society, 2011.Google ScholarGoogle Scholar
  8. D. Bleaken. Botwars: the fight against criminal cyber networks. Comp. Fraud & Sec., 2010.Google ScholarGoogle ScholarCross RefCross Ref
  9. D. Dagon, M. Antonakakis, K. Day, X. Luo, C. P. Lee, and W. Lee. Recursive dns architectures and vulnerability implications. In NDSS. The Internet Society, 2009.Google ScholarGoogle Scholar
  10. M. Felegyhazi, C. Kreibich, and V. Paxson. On the potential of proactive domain blacklisting. In USENIX LEET, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. K. Griffin, S. Schneider, X. Hu, and T.-c. Chiueh. Automatic generation of string signatures for malware detection. In RAID, pages 101--120. Springer, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. N. Jiang, J. Cao, Y. Jin, E. L. Li, and Z.-L. Zhang. Identifying suspicious activities through dns failure graph analysis. In ICNP, pages 144--153, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. M. Lelarge and J. Bolot. Economic incentives to increase security in the internet: The case for insurance. In IEEE INFOCOM, pages 1494--1502, 2009.Google ScholarGoogle ScholarCross RefCross Ref
  14. P. Mockapetris. Domain names: implementation and specification (november 1987). RFC 1035, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. A. Mohaisen and O. Alrawi. Unveiling zeus: automated classification of malware samples. In WWW (Companion Volume), pages 829--832, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Y. Nadji, M. Antonakakis, R. Perdisci, D. Dagon, and W. Lee. Beheading hydras: performing effective botnet takedowns. In ACM CCS, pages 121--132, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. J. Nazario and T. Holz. As the net churns: Fast-flux botnet observations. In IEEE MALWARE, pages 24--31, 2008.Google ScholarGoogle ScholarCross RefCross Ref
  18. P. Porras, H. Saidi, and V. Yegneswaran. Conficker c analysis. SRI International, 2009.Google ScholarGoogle Scholar
  19. C. Roach. Flashback and mac malware. http://bit.ly/1aC1qar, April 2012.Google ScholarGoogle Scholar
  20. S. Shin and G. Gu. Conficker and beyond: a large-scale empirical study. In ACSAC, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. B. Stone-Gross, M. Cova, L. Cavallaro, B. Gilbert, M. Szydlowski, R. Kemmerer, C. Kruegel, and G. Vigna. Your botnet is my botnet: analysis of a botnet takeover. In ACM CCS, pages 635--647, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. S. Yadav, A. K. K. Reddy, A. N. Reddy, and S. Ranjan. Detecting algorithmically generated malicious domain names. In ACM IMC, pages 48--61, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library

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    • Published in

      cover image ACM Other conferences
      WWW '14 Companion: Proceedings of the 23rd International Conference on World Wide Web
      April 2014
      1396 pages
      ISBN:9781450327459
      DOI:10.1145/2567948

      Copyright © 2014 ACM

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      Publication History

      • Published: 7 April 2014

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