Combining Classifiers for Spam Detection

  • Fatiha Barigou
  • Naouel Barigou
  • Baghdad Atmani
Conference paper

DOI: 10.1007/978-3-642-30507-8_8

Volume 293 of the book series Communications in Computer and Information Science (CCIS)
Cite this paper as:
Barigou F., Barigou N., Atmani B. (2012) Combining Classifiers for Spam Detection. In: Benlamri R. (eds) Networked Digital Technologies. NDT 2012. Communications in Computer and Information Science, vol 293. Springer, Berlin, Heidelberg

Abstract

Nowadays e-mail has become a fast and economical way to exchange information. However, unsolicited or junk e-mail also known as spam quickly became a major problem on the Internet and keeping users away from them becomes one of the most important research area. Indeed, spam filtering is used to prevent access to undesirable e-mails. In this paper we propose a spam detection system called “3CA&1NB” which uses machine learning to detect spam. “3CA&1NB” has the characteristic of combining three cellular automata and one naïve Bayes algorithm. We discuss how the combination learning based methods can improve detection performances. Our preliminary results show that it can detect spam effectively.

Keywords

spam cellular automaton Naïve Bayes classifier combination 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Fatiha Barigou
    • 1
  • Naouel Barigou
    • 1
  • Baghdad Atmani
    • 1
  1. 1.Computer Science Laboratory of Oran Computer Science Department, Faculty of ScienceUniversity of OranOranAlgeria