Is data clustering in adversarial settings secure?

Proceedings of the ACM Conference on Computer and Communications Security

Volumn , Issue , 2013, Pages 87-97

  Biggio, Battista ^a   Pillai, Ignazio ^a   Rota Bulò, Samuel ^b   Ariu, Davide ^a   Pelillo, Marcello ^c   Roli, Fabio ^a  

^a UNIVERSITY OF CAGLIARI   (Italy)

^b ITC IRST   (Italy)

^c CA' FOSCARI UNIVERSITY OF VENICE   (Italy)

Author keywords

adversarial learning; clustering; computer security; malware detection; security evaluation; unsupervised learning

Indexed keywords

ADVERSARIAL LEARNING; CLUSTERING; CLUSTERING PROCESS; HANDWRITTEN DIGIT; HIER-ARCHICAL CLUSTERING; MALWARE DETECTION; SECURITY APPLICATION; SECURITY EVALUATION;

ARTIFICIAL INTELLIGENCE; CLUSTER ANALYSIS; COMPUTER CRIME; INPUT OUTPUT PROGRAMS; SECURITY OF DATA; UNSUPERVISED LEARNING;

CLUSTERING ALGORITHMS;

EID: 84888997884     PISSN: 15437221     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2517312.2517321     Document Type: Conference Paper

References (30)

1. Collaborative Malware Collection and Sensing. https://alliance.mwcollect. org.
2. Project Malfease. http://malfease.oarci.net.
3. M. Barreno, B. Nelson, R. Sears, A. D. Joseph, and J. D. Tygar. Can machine learning be secure? In ASIACCS '06: Proc. 2006 ACM Symposium on Information, Computer and Communications Security, pages 16-25, NY, USA, 2006. ACM.
4. U. Bayer, P. M. Comparetti, C. Hlauschek, C. Krügel, and E. Kirda. Scalable, behavior-based malware clustering. In NDSS. The Internet Society, 2009.
5. B. Biggio, G. Fumera, and F. Roli. Design of robust classifiers for adversarial environments. In IEEE Int'l Conf. on Systems, Man, and Cybernetics (SMC), pages 977-982, 2011.
6. B. Biggio, G. Fumera, and F. Roli. Security evaluation of pattern classifiers under attack. IEEE Trans. on Knowledge and Data Eng., 99(PrePrints):1, 2013.
7. B. Biggio, B. Nelson, and P. Laskov. Poisoning attacks against support vector machines. In J. Langford and J. Pineau, editors, 29th Int'l Conf. on Machine Learning. Omnipress, 2012.
8. M. Brückner, C. Kanzow, and T. Scheffer. Static prediction games for adversarial learning problems. J. Mach. Learn. Res., 13:2617-2654, 2012.
9. I. Burguera, U. Zurutuza, and S. Nadjm-Tehrani. Crowdroid: behavior-based malware detection system for android. In Proc. 1st ACM workshop on Security and Privacy in Smartphones and Mobile devices, SPSM '11, pages 15-26, NY, USA, 2011. ACM.
10. C. Castillo and B. D. Davison. Adversarial web search. Foundations and Trends in Information Retrieval, 4(5):377-486, May 2011.
11. J. G. Dutrisac and D. Skillicorn. Hiding clusters in adversarial settings. In IEEE Int'l Conf. on Intelligence and Security Informatics (ISI 2008), pages 185-187, 2008.
12. D. A. Forsyth and J. Ponce. Computer Vision: A Modern Approach. Prentice Hall, 2011.
13. M. Großhans, C. Sawade, M. Brückner, and T. Scheffer. Bayesian games for adversarial regression problems. In J. Mach. Learn. Res. - Proc. 30th Int'l Conf. on Machine Learning (ICML), volume 28, 2013.
14. P. Haider, L. Chiarandini, and U. Brefeld. Discriminative clustering for market segmentation. In Proc. 18th ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining, KDD '12, pages 417-425, NY, USA, 2012. ACM.
15. M. Halkidi, Y. Batistakis, and M. Vazirgiannis. On clustering validation techniques. Journal of Intelligent Information Systems, 17(2-3):107-145, Dec. 2001.
16. S. Hanna, L. Huang, E. Wu, S. Li, C. Chen, and D. Song. Juxtapp: a scalable system for detecting code reuse among android applications. In Proc. 9th Int'l Conf. on Detection of Intrusions and Malware, and Vulnerability Assessment, DIMVA'12, pages 62-81, Berlin, Heidelberg, 2013. Springer-Verlag.
17. L. Huang, A. D. Joseph, B. Nelson, B. Rubinstein, and J. D. Tygar. Adversarial machine learning. In 4th ACM Workshop on Artificial Intelligence and Security (AISec 2011), pages 43-57, Chicago, IL, USA, 2011.
18. A. K. Jain and R. C. Dubes. Algorithms for clustering data. Prentice-Hall, Inc., NJ, USA, 1988.
19. M. Kloft and P. Laskov. Online anomaly detection under adversarial impact. In Proc. 13th Int'l Conf. on Artificial Intell. and Statistics, pages 405-412, 2010.
20. A. Kolcz and C. H. Teo. Feature weighting for improved classifier robustness. In Sixth Conf. on Email and Anti-Spam (CEAS), CA, USA, 2009.
21. Y. LeCun, L. Jackel, L. Bottou, A. Brunot, C. Cortes, J. Denker, H. Drucker, I. Guyon, U. Müller, E. Säckinger, P. Simard, and V. Vapnik. Comparison of learning algorithms for handwritten digit recognition. In Int'l Conf. on Artificial Neural Networks, pages 53-60, 1995.
22. M. Pavan and M. Pelillo. Dominant sets and pairwise clustering. IEEE Trans. on Pattern Analysis and Machine Intelligence, 29(1):167-172, 2007.
23. R. Perdisci, D. Ariu, and G. Giacinto. Scalable fine-grained behavioral clustering of http-based malware. Computer Networks, 57(2):487-500, 2013.
24. R. Perdisci, I. Corona, and G. Giacinto. Early detection of malicious flux networks via large-scale passive DNS traffic analysis. IEEE Trans. on Dependable and Secure Comp., 9(5):714-726, 2012.
25. F. Pouget, M. Dacier, J. Zimmerman, A. Clark, and G. Mohay. Internet attack knowledge discovery via clusters and cliques of attack traces. J. Information Assurance and Security, Vol. 1, Issue 1, March 2006.
26. G. Punj and D. W. Stewart. Cluster analysis in marketing research: Review and suggestions for application. J. Marketing Res., 20(2):134, May 1983.
27. D. B. Skillicorn. Adversarial knowledge discovery. IEEE Intelligent Systems, 24:54-61, 2009.
28. L. Spitzner. Honeypots: Tracking Hackers. Addison-Wesley Professional, 2002.
29. U. von Luxburg. Clustering stability: An overview. Foundations and Trends in Machine Learning, 2(3):235-274, 2010.
30. T. Zhang, R. Ramakrishnan, and M. Livny. Birch: an efficient data clustering method for very large databases. In Proc. 1996 ACM SIGMOD Int'l Conf. on Management of data, SIGMOD '96, pages 103-114, NY, USA, 1996. ACM.