White Papers

Unsupervised Spam Detection Based on String Alienness Measures

Overview This paper proposes an unsupervised method for detecting spam documents from Web page data, based on equivalence relations on strings. They propose 3 measures for quantifying the alienness (i.e. how different it is from others) of substring equivalence classes within a given set of strings. A paper is then classified as spam if it contains a characteristic equivalence class as a substring. The proposed method is unsupervised, independent of language, and is very efficient. Computational experiments conducted on data collected from Japanese web forums show fairly good results.

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Publisher: Kyushu University
File Format: PDF
Date Published: Jul 1, 2009
Format: White Papers
Topics: Spam - E-mail Fraud - Phishing, Network Security, Security Management

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