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A collaborative anti-spam system
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DOI：10.1016/j.eswa.2008.08.075.png)
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Growing volume of spam mails has generated a need for a precise anti-spam filter detecting unsolicited emails. Most works only focus on spam rule generation on a standalone mail server. This paper presents a collaborative framework on spam rule generation, exchange and management. The spam filter can be built based on the mixture of rough set theory, genetic algorithm, and reinforcement learning. In this paper, we use rough set theory to generate spam rules and XML format for exchanging spam rules. The spam rule management is achieved by reinforcement learning approach. The results of experiment draw the following conclusion: (1) Rule management can keep high performance rules and discard out-of-date rules to improve the accuracy and efficiency of spam filter. (2) Rules exchanged among mail servers indeed help the spam filter block more spam messages than standalone one. (C) 2008 Elsevier Ltd. All rights reserved.
Keyword:
Spam mail
Rough Set theory
Reinforcement learning
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7.5
论文数:
2.9W
被引数:
10.2W
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