Density-based spam detector

Kenichi Yoshida, Fuminori Adachi, Takashi Washio, Hiroshi Motoda, Teruaki Homma, Akihiro Nakashima, Hiromitsu Fujikawa, Katsuyuki Yamazaki

Abstract: 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.