Adversarial learning

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Adversarial learning

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International Conference on Knowledge Discovery and Data Mining archive
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining table of contents

Chicago, Illinois, USA

POSTER SESSION: Research track poster table of contents

Pages: 641 - 647

Year of Publication: 2005

ISBN:1-59593-135-X

Authors

Daniel Lowd	University of Washington - Seattle, Seattle, WA
Christopher Meek	Microsoft Research, Redmond, WA

Sponsors

SIGKDD: ACM Special Interest Group on Knowledge Discovery in Data
ACM: Association for Computing Machinery

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 7, Downloads (12 Months): 70, Citation Count: 7

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ABSTRACT

Many classification tasks, such as spam filtering, intrusion detection, and terrorism detection, are complicated by an adversary who wishes to avoid detection. Previous work on adversarial classification has made the unrealistic assumption that the attacker has perfect knowledge of the classifier [2]. In this paper, we introduce the adversarial classifier reverse engineering (ACRE) learning problem, the task of learning sufficient information about a classifier to construct adversarial attacks. We present efficient algorithms for reverse engineering linear classifiers with either continuous or Boolean features and demonstrate their effectiveness using real data from the domain of spam filtering.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	Dana Angluin, Queries and Concept Learning, Machine Learning, v.2 n.4, p.319-342, April 1988 [doi>10.1023/A:1022821128753]
	2	Nilesh Dalvi , Pedro Domingos , Mausam , Sumit Sanghai , Deepak Verma, Adversarial classification, Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, August 22-25, 2004, Seattle, WA, USA [doi>10.1145/1014052.1014066]
	3	D. Lowd and C. Meek. Good word attacks on statistical spam filters. In Proceedings of the Second Conference on Email and Anti-Spam, Palo Alto, CA, 2005.
	4	M. Sahami, S. Dumais, D. Heckerman, and E. Horvitz. A Bayesian approach to filtering junk E-mail. In Learning for Text Categorization: Papers from the 1998 Workshop, Madison, Wisconsin, 1998. AAAI Technical Report WS-98-05.
	5	S. Tzu. The art of war, 500bc.
	6	L. G. Valiant, A theory of the learnable, Communications of the ACM, v.27 n.11, p.1134-1142, Nov. 1984 [doi>10.1145/1968.1972]
	7	L. Zhang and T. Yao. Filtering junk mail with a maximum entropy model. In ICCPOL2003, pages 446--453, ShenYang, China, 2003.

CITED BY 7

	Marco Barreno , Blaine Nelson , Russell Sears , Anthony D. Joseph , J. D. Tygar, Can machine learning be secure?, Proceedings of the 2006 ACM Symposium on Information, computer and communications security, March 21-24, 2006, Taipei, Taiwan
	Blaine Nelson , Marco Barreno , Fuching Jack Chi , Anthony D. Joseph , Benjamin I. P. Rubinstein , Udam Saini , Charles Sutton , J. D. Tygar , Kai Xia, Exploiting machine learning to subvert your spam filter, Proceedings of the 1st Usenix Workshop on Large-Scale Exploits and Emergent Threats, p.1-9, April 15-15, 2008, San Francisco, California
	Marco Barreno , Peter L. Bartlett , Fuching Jack Chi , Anthony D. Joseph , Blaine Nelson , Benjamin I.P. Rubinstein , Udam Saini , J. D. Tygar, Open problems in the security of learning, Proceedings of the 1st ACM workshop on Workshop on AISec, October 27-27, 2008, Alexandria, Virginia, USA
	Ming-wei Chang , Wen-tau Yih , Christopher Meek, Partitioned logistic regression for spam filtering, Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, August 24-27, 2008, Las Vegas, Nevada, USA
	Clifton Phua , Vincent Lee , Kate Smith-Miles , Ross Gayler, Adaptive communal detection in search of adversarial identity crime, Proceedings of the 2007 international workshop on Domain driven data mining, p.1-10, August 12-12, 2007, San Jose, California
	Zach Jorgensen , Yan Zhou , Meador Inge, A Multiple Instance Learning Strategy for Combating Good Word Attacks on Spam Filters, The Journal of Machine Learning Research, 9, p.1115-1146, 6/1/2008
	Benjamin Liebald , Dan Roth , Neelay Shah , Vivek Srikumar, Proactive intrusion detection, Proceedings of the 23rd national conference on Artificial intelligence, p.772-777, July 13-17, 2008, Chicago, Illinois