Multi-objective spam filtering using an evolutionary algorithm

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) Pub Date : 2008-06-01 DOI:10.1109/CEC.2008.4630786

James Dudley, L. Barone, R. L. While

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引用次数: 15

Abstract

SpamAssassin is a widely-used open source heuristic-based spam filter that applies a large number of weighted tests to a message, sums the results of the tests, and labels the message as spam if the sum exceeds a user-defined threshold. Due to the large number of tests and the interactions between them, defining good weights for SpamAssassin is difficult: moreover, users with different needs may desire different sets of weights to be used. We have built a multi-objective evolutionary algorithm MOSF that evolves weights for the tests in SpamAssassin according to two independent objectives: minimising the number of false positives (legitimate messages mislabeled as spam), and minimising the number of false negatives (spam messages mislabeled as legitimate). We show that MOSF returns a set of solutions offering a range of setups for SpamAssassin satisfying different userspsila needs, and also that MOSF can derive solutions which beat the existing SpamAssassin weights in both objectives simultaneously. Applying these ideas could substantially increase the usefulness of SpamAssassin and similar systems.

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基于进化算法的多目标垃圾邮件过滤

SpamAssassin是一种广泛使用的基于启发式的开源垃圾邮件过滤器，它对消息应用大量加权测试，对测试结果求和，如果总和超过用户定义的阈值，则将该消息标记为垃圾邮件。由于大量的测试和它们之间的交互，为SpamAssassin定义良好的权重是困难的:此外，具有不同需求的用户可能希望使用不同的权重集。我们已经构建了一个多目标进化算法MOSF，它根据两个独立的目标为SpamAssassin中的测试进化权重:最小化误报(合法消息被误标记为垃圾邮件)的数量，以及最小化误报(垃圾消息被误标记为合法)的数量。我们展示了MOSF返回的一组解决方案为满足不同用户需求的SpamAssassin提供了一系列设置，而且MOSF还可以在两个目标中同时获得优于现有SpamAssassin权重的解决方案。应用这些想法可以大大提高SpamAssassin和类似系统的实用性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)

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