Using statistical machine learning for making security decisions introduces new vulnerabilities in large scale systems. We show how an adversary can exploit statistical machine learning, as used in the SpamBayes spam filter, to render it useless—even if the adversary’s access is limited to only 1% of the spam training messages. We demonstrate three new attacks that successfully make the filter unusable, prevent victims from receiving specific email messages, and cause spam emails to arrive in the victim’s inbox.
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References
Barreno M, Nelson B, Sears R, Joseph AD, Tygar JD (2006) Can machine learning be secure? In: Proceedings of the ACM Symposium on InformAtion, Computer, and Communications Security (ASIACCS), pp 16-25
Barreno M, Nelson, Joseph AD, Tygar JD (2008) The security of machine learning. Tech. Rep. UCB/EECS-2008-43, EECS Department, University of California, Berkeley, URL http://www.eecs.berkeley.edu/Pubs/TechRpts/2008/EECS-2008-43.html
Chung SP, Mok AK (2006) Allergy attack against automatic signature generation. In: Proceedings of the International Symposium on Recent Advances in Intrusion Detection (RAID), pp 61-80
Chung SP, Mok AK (2007) Advanced allergy attacks: Does a corpus really help? In: Proceedings of the International Symposium on Recent Advances in Intrusion Detection (RAID), pp 236-255
Cormack G, Lynam T (2005) Spam corpus creation for TREC. In: Proceedings of the Conference on Email and Anti-Spam (CEAS)
Dalvi N, Domingos P, Mausam, Sanghai S, Verma D (2004) Adversarial classification. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 99-108
Fisher RA (1948) Question 14: Combining independent tests of significance. American Statistician 2(5):30-30J
Graham P (2002) A plan for spam. http://www.paulgraham.com/spam.html
Karlberger C, Bayler G, Kruegel C, Kirda E (2007) Exploiting redundancy in natural language to penetrate Bayesian spam filters. In: Proceedings of the USENIX Workshop on Offensive Technologies (WOOT), pp 1-7
Kearns M, Li M (1993) Learning in the presence of malicious errors. SIAM Journal on Computing 22(4):807-837
Kim HA, Karp B (2004) Autograph: Toward automated, distributed worm signature detection. In: Proceedings of the USENIX Security Symposium, pp 271-286
Klimt B, Yang Y (2004) Introducing the Enron corpus. In: Proceedings of the Conference on Email and Anti-Spam (CEAS)
Lazarevic A, Ertöz L, Kumar V, Ozgur A, Srivastava J (2003) A comparative study of anomaly detection schemes in network intrusion detection. In: Barbará D, Kamath C (eds) Proceedings of the SIAM International Conference on Data Mining, pp 25-36
Liao Y, Vemuri VR (2002) Using text categorization techniques for intrusion detection. In: Proceedings of the USENIX Security Symposium, pp 51-59
Lowd D, Meek C (2005) Adversarial learning. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 641-647
Lowd D, Meek C (2005) Good word attacks on statistical spam filters. In: Proceedings of the Conference on Email and Anti-Spam (CEAS)
Meyer T, Whateley B (2004) SpamBayes: Effective open-source, Bayesian based, email classification system. In: Proceedings of the Conference on Email and Anti-Spam (CEAS)
Mukkamala S, Janoski G, Sung A (2002) Intrusion detection using neural networks and support vector machines. In: Proceedings of the International Joint Conference on Neural Networks (IJCNN), pp 1702-1707
Nelson B, Barreno M, Chi FJ, Joseph AD, Rubinstein BIP, Saini U, Sutton C, Tygar JD, Xia K (2008) Exploiting machine learning to subvert your spam filter. In: Proceedings of the USENIX Workshop on Large-Scale Exploits and Emergent Threats (LEET)
Newsome J, Karp B, Song D (2005) Polygraph: Automatically generating signatures for polymorphic worms. In: Proceedings of the IEEE Symposium on Security and Privacy, pp 226-241
Newsome J, Karp B, Song D (2006) Paragraph: Thwarting signature learning by training maliciously. In: Proceedings of the International Symposium on Recent Advances in Intrusion Detection (RAID 2006), pp 81-105
Robinson G (2003) A statistical approach to the spam problem. Linux Journal
Shaoul C, Westbury C (2007) A USENET corpus (2005-2007)
Stolfo SJ, Li WJ, Hershkop S, Wang K, Hu CW, Nimeskern O (2004) Detecting viral propagations using email behavior profiles. ACM Transactions on Internet Technology (TOIT) pp 187-221
Wittel GL, Wu SF (2004) On attacking statistical spam filters. In: Proceedings of the Conference on Email and Anti-Spam (CEAS)
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Nelson, B. et al. (2009). Misleading Learners: Co-opting Your Spam Filter. In: Machine Learning in Cyber Trust. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-88735-7_2
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DOI: https://doi.org/10.1007/978-0-387-88735-7_2
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