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Architecture of Adaptive Spam Filtering Based on Machine Learning Algorithms

  • Md Rafiqul Islam
  • Wanlei Zhou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4494)

Abstract

Spam is commonly defined as unsolicited email messages and the goal of spam filtering is to distinguish between spam and legitimate email messages. Much work has been done to filter spam from legitimate emails using machine learning algorithm and substantial performance has been achieved with some amount of false positive (FP) tradeoffs. In the case of spam detection FP problem is unacceptable sometimes. In this paper, an adaptive spam filtering model has been proposed based on Machine learning (ML) algorithms which will get better accuracy by reducing FP problems. This model consists of individual and combined filtering approach from existing well known ML algorithms. The proposed model considers both individual and collective output and analyzes them by an analyzer. A dynamic feature selection (DFS) technique also proposed in this paper for getting better accuracy.

Keywords

Machine learning spam SVM NB FP 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Md Rafiqul Islam
    • 1
  • Wanlei Zhou
    • 1
  1. 1.School of Engineering and Information Technology, Deakin University, MelbourneAustralia

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