Hybrid email spam detection model with negative selection algorithm and differential evolution
Email spam is an increasing problem that not only affects normal users of internet but also causes a major problem for companies and organizations. Earlier techniques have been impaired by the adaptive nature of unsolicited email spam. Inspired by adaptive algorithm, this paper introduces a modified machine learning technique of the human immune system called negative selection algorithm (NSA). A local selection differential evolution (DE) generates detectors at the random detector generation phase of NSA; code named NSADE. Local outlier factor (LOF) is implemented as fitness function to maximize the distance of generated spam detectors from the non-spam space. The problem of overlapping detectors is also solved by calculating the minimum and maximum distance of two overlapped detectors in the spam space. From the experiments, the results show that the detection accuracy of NSADE is 83.06% while the standard negative selection algorithm is 68.86% at 7000 generated detectors.
email spam negative selection algorithm differential evolution detector generation non-spam