Large margin classification for combating disguise attacks on spam filters

Xi-chuan ZHOU; Hai-bin SHEN; Zhi-yong HUANG; Guo-jun LI

doi:10.1631/jzus.C1100259

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Large margin classification for combating disguise attacks on spam filters

计算机与自动化 | Updated：2022-12-09

- Large margin classification for combating disguise attacks on spam filters
- Large margin classification for combating disguise attacks on spam filters
- 信息与电子工程前沿（英文） 2012年13卷第3期页码：187-195
- Affiliations：
  
  1.College of Communications Engineering, Chongqing University, Chongqing 400044, China
  2.Institute of VLSI Design, Zhejiang University, Hangzhou 310027, China
  3.Chongqing Communication Institute, Chongqing 400032, China
- Author bio：
  
  [ "Xi-chuan ZHOU, zxc@cqu.edu.cn" ]
- Funds：
  
  Project supported by the National Natural Science Foundation of China (No. 61103212) and the Natural Science Foundation of CQ CSTC, China (No. cstcjjA40005)
- DOI：10.1631/jzus.C1100259
  中图分类号： TP393.098
- 收稿：2011-09-02，
  
  修回：2012-;2-;8，
  
  纸质出版：2012-03
- Accepted：
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Large margin classification for combating disguise attacks on spam filters[J]. 信息与电子工程前沿（英文）, 2012,13(3):187-195.

Xi-chuan ZHOU, Hai-bin SHEN, Zhi-yong HUANG, et al. Large margin classification for combating disguise attacks on spam filters[J]. Frontiers of Information Technology & Electronic Engineering, 2012, 13(3): 187-195.
Large margin classification for combating disguise attacks on spam filters[J]. 信息与电子工程前沿（英文）, 2012,13(3):187-195. DOI： 10.1631/jzus.C1100259.

Xi-chuan ZHOU, Hai-bin SHEN, Zhi-yong HUANG, et al. Large margin classification for combating disguise attacks on spam filters[J]. Frontiers of Information Technology & Electronic Engineering, 2012, 13(3): 187-195. DOI： 10.1631/jzus.C1100259.

摘要

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.

关键词

Keywords

references

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