Unsupervised spam detection by document probability estimation with maximal overlap method

Takashi Uemura, Daisuke Ikeda, Takuya Kida, Hiroki Arimura

Research output: Contribution to journalArticle

Abstract

In this paper, we study content-based spam detection for spams that are generated by copying a seed document with some random perturbations. We propose an unsupervised detection algorithm based on an entropy-like measure called document complexity, which reflects how many similar documents exist in the input collection of documents. As the document complexity, however, is an ideal measure like Kolmogorov complexity, we substitute an estimated occurrence probability of each document for its complexity. We also present an efficient algorithm that estimates the probabilities of all documents in the collection in linear time to its total length. Experimental results showed that our algorithm especially works well for word salad spams, which are believed to be difficult to detect automatically.

Original languageEnglish
Pages (from-to)297-306
Number of pages10
JournalTransactions of the Japanese Society for Artificial Intelligence
Volume26
Issue number1
DOIs
Publication statusPublished - Jan 13 2011

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

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