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首页> 外文会议>International Conference on Cybernetics and Information Technologies, Systems and Applications(CITSA2004) and 10th International Conference on Information Systems Analysis and Synthesis(ISAS2004) vol.1 >Spam Detection Using Statistical Theorem
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Spam Detection Using Statistical Theorem

机译：使用统计定理检测垃圾邮件

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摘要

Bayesian filtering is an anti-spam algorithm which is designed to tackle spam dealing with probabilities. It is named after Bayes' Theorem of statistics, which is used to calculate the probability whether a message is a spam or not. This filter works efficiently by comparing e-mail content (phrases or tokens) against stored databases. This paper proposes Binomial Distribution and Poisson Distribution to be implemented in Bayesian spam filter. This approach is beneficial for calculating the probability of a mail being spam containing words that are not stored in database (i.e., encountered by the spam filter for the first time) or rare words (less frequent words). The proposed distribution for spam filters reduces and controls the false positives.
机译：贝叶斯过滤是一种反垃圾邮件算法，旨在解决垃圾邮件处理概率问题。它以贝叶斯统计定理命名，该定理用于计算邮件是否为垃圾邮件的概率。通过将电子邮件内容（短语或令牌）与存储的数据库进行比较，此筛选器可以有效地工作。本文提出了在贝叶斯垃圾邮件过滤器中要实现的二项分布和泊松分布。这种方法对于计算邮件是垃圾邮件的概率是很有用的，该垃圾邮件包含未存储在数据库中的单词（即垃圾邮件过滤器首次遇到的单词）或稀有单词（频率较低的单词）。建议的垃圾邮件过滤器分发减少并控制了误报。

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