Personalized Filtering of Polymorphic E-mail Spam

Masaru Takesue
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引用次数: 2

Abstract

Which of emails are spams depends on the recipient's interest, so it is desirable to filter spams based on his/her interest. We store the fingerprints (FPs) of k portions of each spam's content in our filter and examine the metrics for detecting the polymorphic spams devised with intent to thwart the detection. For a smaller size of the filter, we exploit two Bloom filters (in fact, merged into a single one to reduce cache miss) to replace the least recently matched spams by recently matched ones. We use as the metrics the number $N_t (≤ k)$ of FPs in the filter matching with those of an incoming email, but also of the $N_T$ FPs, the greatest number $N_d$ of FPs stored for a single spam. We plot spams and legitimate emails in the $N_d-N_t$ space and detect spams by a piecewise linear function. The experiments with about 4,000 real world emails show that our filter achieves the false negative rate of about 0.36 with no false positive.
多态垃圾邮件的个性化过滤
哪些电子邮件是垃圾邮件取决于收件人的兴趣,因此最好根据他/她的兴趣来过滤垃圾邮件。我们将每个垃圾邮件内容的k个部分的指纹(FPs)存储在我们的过滤器中,并检查用于检测旨在阻止检测的多态垃圾邮件的指标。对于较小尺寸的过滤器,我们利用两个Bloom过滤器(实际上,合并为一个过滤器以减少缓存丢失),用最近匹配的垃圾邮件替换最近匹配最少的垃圾邮件。我们使用过滤器中与传入电子邮件匹配的FPs数$N_t(≤k)$作为度量,还使用$N_t $ FPs作为度量,即单个垃圾邮件中存储的最大FPs数$N_d$。我们在$N_d-N_t$空间中绘制垃圾邮件和合法电子邮件,并通过分段线性函数检测垃圾邮件。对约4000封真实邮件的实验表明,我们的过滤器达到了约0.36的假阴性率,没有假阳性。
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