Density-based spam detector-猫眼课题宝

DOI：

10.1145/1014052.1014107

发表时间：

2004-08

期刊：

Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining

影响因子：

通讯作者：

中科院分区：

其他

文献类型：

作者：

Kenichi Yoshida;Fuminori Adachi;T. Washio;H. Motoda;Teruaki Homma;Akihiro Nakashima;Hiromitsu Fujikawa;Katsuyuki Yamazaki

大量未经请求的电子邮件（通常称为垃圾邮件）的数量最近急剧增加，不仅对互联网而且对社会构成严重威胁。本文提出了一种利用文档空间密度信息的新垃圾邮件检测方法。尽管需要大量的电子邮件流量来获取必要的信息，但具有短白名单的无监督学习引擎可以实现 98% 的召回率和 100% 的准确率。直接映射缓存方法每秒可处理超过 13,000 封电子邮件。本文还报告了使用超过 5000 万封实际电子邮件流量进行的实验结果。

The volume of mass unsolicited electronic mail, often known as spam, has recently increased enormously and has become a serious threat to not only the Internet but also to society. This paper proposes a new spam detection method which uses document space density information. Although it requires extensive e-mail traffic to acquire the necessary information, an unsupervised learning engine with a short white list can achieve a 98% recall rate and 100% precision. A direct-mapped cache method contributes handling of over 13,000 e-mails per second. Experimental results, which were conducted using over 50 million actual e-mails of traffic, are also reported in this paper.

Density-based spam detector

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