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An Spam Discrimination Based on Mail Header Feature and SVM

机译:基于邮件头特征和SVM的垃圾邮件识别

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摘要

The traditional anti-spam techniques like Black and White List can not meet the needs of the Spam Filter nowadays. Some Machine Learning techniques become very popular in the research of Spam Filter. Support Vector Machine is one of the most excellent methods in classifying. But these techniques are usually applied to spam identity based on the mail body textual content only, seldom discussing about mail header. This paper hereby proposes the Spam Discrimination Model based on SVM, and uses SVM to sort out mail according to the feature of mail headers. By feature abstraction carried out on the mails dataset (CDSCE) with C++program and SVM classifying, Experimental result indicates that the proposed model can effectively improve the accuracy of spam identification.
机译:诸如黑白名单之类的传统反垃圾邮件技术已不能满足当今垃圾邮件过滤器的需求。一些机器学习技术在垃圾邮件过滤器的研究中变得非常流行。支持向量机是分类中最出色的方法之一。但是这些技术通常仅基于邮件正文文本内容应用于垃圾邮件标识,很少讨论邮件头。本文提出了一种基于支持向量机的垃圾邮件识别模型,并根据邮件头的特征使用支持向量机对邮件进行分类。通过使用C ++程序和SVM分类对邮件数据集(CDSCE)进行特征抽象,实验结果表明,该模型可以有效提高垃圾邮件识别的准确性。

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