CLC number: TP393.098
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2012-02-08
Cited: 1
Clicked: 7710
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.
@article{title="Large margin classification for combating disguise attacks on spam filters",
author="Xi-chuan Zhou, Hai-bin Shen, Zhi-yong Huang, Guo-jun Li",
journal="Journal of Zhejiang University Science C",
volume="13",
number="3",
pages="187-195",
year="2012",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.C1100259"
}
%0 Journal Article
%T Large margin classification for combating disguise attacks on spam filters
%A Xi-chuan Zhou
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%A Zhi-yong Huang
%A Guo-jun Li
%J Journal of Zhejiang University SCIENCE C
%V 13
%N 3
%P 187-195
%@ 1869-1951
%D 2012
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C1100259
TY - JOUR
T1 - Large margin classification for combating disguise attacks on spam filters
A1 - Xi-chuan Zhou
A1 - Hai-bin Shen
A1 - Zhi-yong Huang
A1 - Guo-jun Li
J0 - Journal of Zhejiang University Science C
VL - 13
IS - 3
SP - 187
EP - 195
%@ 1869-1951
Y1 - 2012
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.C1100259
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.
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