Title:	SPAM FILTERING USING COMPRESSION
Author(s):	M. Farmer , G. Richard , F. Faure , M. Lopusniac
ISBN:	978-972-8924-49-2
Editors:	Sandeep Krishnamurthy and Pedro Isaías
Year:	2007
Edition:	Single
Keywords:	Spam, Kolmogorov Complexity, Compression, Clustering, K-Nearest Neighbors
Type:	Full Paper
First Page:	67
Last Page:	74
Language:	English
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Full Contents:	click to dowload
Paper Abstract:	One of the most irrelevant side effects of the e-commerce technology is the development of spamming as an e-marketing technique. Spam emails (or unsolicited commercial emails) induce a burden for everybody having an electronic mailbox: detecting and filtering spam is then a challenging task and a lot of approaches have been developed to identify a spam before it is posted in the end user mailbox. In this paper, we focus on a relatively new approach whose foundations rely on the works of A. Kolmogorov. The main idea is to give a formal meaning to the notion of “information content” and to provide a measure of this content. Using such a quantitative approach, it becomes possible to define a distance which is a major tool for classification purpose. To validate our approach, we proceed in two steps: first we use the classical compression distance over a mix of spam and legitimate emails to check out if they can be properly clustered without any supervision. It has been the case, highlighting a kind of underlying structure for the spam emails. In a second step, we implement a k-nearest neighbors algorithm providing 85% as accuracy rate. Coupled with other anti-spam techniques, compression-based methods could bring a great help in the spam filtering challenge.
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