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Front. Inform. Technol. Electron. Eng.  2012, Vol. 13 Issue (3): 187-195    DOI: 10.1631/jzus.C1100259
    
Large margin classification for combating disguise attacks on spam filters
Xi-chuan Zhou, Hai-bin Shen, Zhi-yong Huang, Guo-jun Li
College of Communications Engineering, Chongqing University, Chongqing 400044, China; Institute of VLSI Design, Zhejiang University, Hangzhou 310027, China; Chongqing Communication Institute, Chongqing 400032, China
Large margin classification for combating disguise attacks on spam filters
Xi-chuan Zhou, Hai-bin Shen, Zhi-yong Huang, Guo-jun Li
College of Communications Engineering, Chongqing University, Chongqing 400044, China; Institute of VLSI Design, Zhejiang University, Hangzhou 310027, China; Chongqing Communication Institute, Chongqing 400032, China
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摘要： 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.
关键词： Large margin;  Spam filtering;  Second-order cone programming (SOCP);  Adversarial classification    
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.
Key words: Large margin    Spam filtering    Second-order cone programming (SOCP)    Adversarial classification
收稿日期: 2011-09-02 出版日期: 2012-03-01
CLC:  TP393.098  
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Xi-chuan Zhou
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引用本文:

Xi-chuan Zhou, Hai-bin Shen, Zhi-yong Huang, Guo-jun Li. Large margin classification for combating disguise attacks on spam filters. Front. Inform. Technol. Electron. Eng., 2012, 13(3): 187-195.

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http://www.zjujournals.com/xueshu/fitee/CN/10.1631/jzus.C1100259    或    http://www.zjujournals.com/xueshu/fitee/CN/Y2012/V13/I3/187

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