Email Spam Detection Using Differential Evolution Negative Selection Algorithm

Provided by: AICIT
Topic: Security
Format: PDF
In this paper, the authors 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.

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