Email Spam Detection Using Differential Evolution Negative Selection Algorithm

Abstract

In this paper, we propose a modification of machine learning techniques inspired by human immune system called negative selection algorithm (NSA) with differential evolution (DE) code-name NSA-DE; in order to deal with the growing problem of unsolicited email in the mail box. The evolutionary algorithm generates detectors at the random detector generation phase of negative selection algorithm. NSA-DE uses local differential evolution for detector generation and local outlier factor (LOF) as fitness function to maximize the distance between generated detector and non-spam space. The theoretical analysis and the experimental result show that the proposed NSA-DE model performs better than the standard NSA.