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CLC number: TP393.098

On-line Access: 2012-03-01

Received: 2011-09-02

Revision Accepted: 2011-10-25

Crosschecked: 2012-02-08

Cited: 1

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Journal of Zhejiang University SCIENCE C 2012 Vol.13 No.3 P.187-195

10.1631/jzus.C1100259


Large margin classification for combating disguise attacks on spam filters


Author(s):  Xi-chuan Zhou, Hai-bin Shen, Zhi-yong Huang, Guo-jun Li

Affiliation(s):  College of Communications Engineering, Chongqing University, Chongqing 400044, China; more

Corresponding email(s):   zxc@cqu.edu.cn

Key Words:  Large margin, Spam filtering, Second-order cone programming (SOCP), Adversarial classification


Xi-chuan Zhou, Hai-bin Shen, Zhi-yong Huang, Guo-jun Li. Large margin classification for combating disguise attacks on spam filters[J]. Journal of Zhejiang University Science C, 2012, 13(3): 187-195.

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Abstract: 
This paper addresses the challenge of large margin classification for spam filtering in the presence of an adversary who disguises the spam mails to avoid being detected. In practice, the adversary may strategically add good words indicative of a legitimate message or remove bad words indicative of spam. We assume that the adversary could afford to modify a spam message only to a certain extent, without damaging its utility for the spammer. Under this assumption, we present a large margin approach for classification of spam messages that may be disguised. The proposed classifier is formulated as a second-order cone programming optimization. We performed a group of experiments using the TREC 2006 Spam Corpus. Results showed that the performance of the standard support vector machine (SVM) degrades rapidly when more words are injected or removed by the adversary, while the proposed approach is more stable under the disguise attack.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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