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k-NN Classification of Malware in HTTPS Traffic Using the Metric Space Approach

  • Jakub Lokoč
  • Jan Kohout
  • Přemysl Čech
  • Tomáš Skopal
  • Tomáš Pevný
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9650)

Abstract

In this paper, we present detection of malware in HTTPS traffic using k-NN classification. We focus on the metric space approach for approximate k-NN searches over dataset of sparse high-dimensional descriptors of network traffic. We show the classification based on approximate k-NN search using metric index exhibits false positive rate reduced by an order of magnitude when compared to the state of the art method, while keeping the classification fast enough.

Keywords

Similarity search k-NN classification Intrusion detection 

Notes

Acknowledgments

This research has been supported by Czech Science Foundation project (GAČR) 15-08916S.

References

  1. 1.
    Brin, S.: Near neighbor search in large metric spaces. In: Proceedings of 21th International Conference on Very Large Data Bases, VLDB 1995, 11–15 September 1995, Zurich, Switzerland, pp. 574–584 (1995). http://www.vldb.org/conf/1995/P574.PDF
  2. 2.
    Chaudhuri, K., Dasgupta, S.: Rates of convergence for nearest neighbor classification. In: Advances in Neural Information Processing Systems (2014)Google Scholar
  3. 3.
    Chávez, E., Navarro, G.: A compact space decomposition for effective metric indexing. Pattern Recogn. Lett. 26(9), 1363–1376 (2005). http://dx.doi.org/10.1016/j.patrec.2004.11.014 CrossRefGoogle Scholar
  4. 4.
    Chávez, E., Navarro, G., Baeza-Yates, R., Marroquín, J.L.: Searching in metric spaces. ACM Comput. Surv. 33(3), 273–321 (2001)CrossRefGoogle Scholar
  5. 5.
    Ciaccia, P., Patella, M., Zezula, P.: M-tree: an efficient access method for similarity search in metric spaces. In: VLDB 1997, pp. 426–435 (1997)Google Scholar
  6. 6.
  7. 7.
  8. 8.
    Claise, B., Trammell, B., Aitken, P.: Specification of the IP Flow Information Export (IPFIX) Protocol for the Exchange of Flow Information (2013). https://tools.ietf.org/html/rfc7011
  9. 9.
    Crotti, M., Dusi, M., Gringoli, F., Salgarelli, L.: Traffic classification through simple statistical fingerprinting. SIGCOMM Comput. Commun. Rev. 37, 5–16 (2007)CrossRefGoogle Scholar
  10. 10.
    Dusi, M., Crotti, M., Gringoli, F., Salgarelli, L.: Tunnel hunter: detecting application-layer tunnels with statistical fingerprinting. Comput. Netw. 53, 81–97 (2009)CrossRefGoogle Scholar
  11. 11.
    Fielding, R., Gettys, J., Mogul, J., Frystyk, H., Masinter, L., Leach, P., Berners-Lee, T.: Hypertext Transfer Protocol – HTTP/1.1. https://tools.ietf.org/html/rfc2616
  12. 12.
    Gionis, A., Indyk, P., Motwani, R.: Similarity search in high dimensions via hashing. In: Proceedings of the 25th International Conference on Very Large Data Bases, VLDB 1999, pp. 518–529. Morgan Kaufmann Publishers Inc., San Francisco (1999). http://dl.acm.org/citation.cfm?id=645925.671516
  13. 13.
    Kohout, J., Pevny, T.: Automatic discovery of web servers hosting similar applications. In: 2015 IFIP/IEEE International Symposium on Integrated Network Management (IM) (2015)Google Scholar
  14. 14.
    Kohout, J., Pevny, T.: Unsupervised detection of malware in persistent web traffic. In: 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2015)Google Scholar
  15. 15.
    van der Maaten, L., Hinton, G.E.: Visualizing high-dimensional data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008)zbMATHGoogle Scholar
  16. 16.
    Nelms, T., Perdisci, R., Ahamad, M.: Execscent: mining for new c&c domains in live networks with adaptive control protocol templates. In: Proceedings of the 22nd USENIX Conference on Security (2013)Google Scholar
  17. 17.
    Novak, D., Batko, M., Zezula, P.: Metric index: an efficient and scalable solution for precise and approximate similarity search. Inf. Syst. 36(4), 721–733 (2011)CrossRefGoogle Scholar
  18. 18.
    Novak, D., Kyselak, M., Zezula, P.: On locality-sensitive indexing in generic metric spaces. In: Proceedings of the Third International Conference on SImilarity Search and APplications, SISAP 2010, pp. 59–66. ACM, New York (2010). http://doi.acm.org/10.1145/1862344.1862354
  19. 19.
    Perdisci, R., Ariu, D., Giacinto, G.: Scalable fine-grained behavioral clustering of HTTP-based malware. Comput. Netw. 57, 487–500 (2013)CrossRefGoogle Scholar
  20. 20.
    Perdisci, R., Lee, W., Feamster, N.: Behavioral clustering of HTTP-based malware and signature generation using malicious network traces. In: Proceedings of the 7th USENIX Conference on Networked Systems Design and Implementation (2010)Google Scholar
  21. 21.
    Pevny, T., Ker, A.D.: Towards dependable steganalysis. In: IS&T/SPIE Electronic Imaging (2015)Google Scholar
  22. 22.
    Wright, C., Monrose, F., Masson, G.M.: On inferring application protocol behaviors in encrypted network traffic. J. Mach. Learn. Res. 7, 2745–2769 (2006)MathSciNetzbMATHGoogle Scholar
  23. 23.
    Zezula, P., Amato, G., Dohnal, V., Batko, M.: Similarity Search: The Metric Space Approach. Springer, New York (2005)zbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Jakub Lokoč
    • 1
  • Jan Kohout
    • 2
  • Přemysl Čech
    • 1
  • Tomáš Skopal
    • 1
  • Tomáš Pevný
    • 2
  1. 1.SIRET Research Group, Department of Software Engineering, Faculty of Mathematics and PhysicsCharles University in PraguePragueCzech Republic
  2. 2.Department of Computer Science and Engineering, FEECzech Technical University in Prague, Cisco Systems, Inc., Cognitive Research Center in PraguePragueCzech Republic

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