Learning spam: simple techniques for freely-available software

Authors:
Bart Massey;Mick Thomure;Raya Budrevich;Scott Long
Affiliations:
Computer Science Department, Portland State University, Portland, OR;Computer Science Department, Portland State University, Portland, OR;Computer Science Department, Portland State University, Portland, OR;Computer Science Department, Portland State University, Portland, OR
Venue:
ATEC '03 Proceedings of the annual conference on USENIX Annual Technical Conference
Year:
2003

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Abstract

The problem of automatically filtering out spam email using a classifier based on machine learning methods is of great recent interest. This paper gives an introduction to machine learning methods for spam filtering, reviewing some of the relevant ideas and work in the open source community. An overview of several feature detection and machine learning techniques for spam filtering is given. The authors' freely-available implementations of these techniques are discussed. The techniques' performance on several different corpora are evaluated. Finally, some conclusions are drawn about the state of the art and about fruitful directions for spam filtering for freely-available UNIX software practitioners.