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Improved Negative Selection Algorithm with Application to Email Spam Detection

  • Ismaila Idris
  • Ali Selamat
Part of the Studies in Computational Intelligence book series (SCI, volume 551)

Abstract

Email spam is an unsolicited message sent into the mail box; it requires the need of an adaptive spam detection model to eliminate these unwanted messages. The random detector generation in negative selection algorithm (NSA) is improved upon by the implementation of particle swarm optimization (PSO) to generate detectors. In the process of detector generation, a fitness function that calculates the reach-ability distance between the non-spam space and the candidate detector, and use the result of the reach-ability distance to calculate the local density factor among the candidate detector was also implemented. The result of the experiment shows an enhancement of the improved NSA over the standard NSA.

Keywords

Negative selection algorithm Particle swarm optimization Local outlier factor Spam Non-spam 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Software Engineering Department, Faculty of ComputingUniversiti Tecknologi MalaysiaJohor BahruMalaysia

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