Model and Algorithm in Artificial Immune System for Spam Detection

Abstract

A spam detection model based on negative selection algorithm is proposed in this paper. The artificial immune system creates techniques to solve complex computations, aiming to developing immune based models. This is done by distinguishing self from non-self. Preliminary mathematical analysis will expose the computation and experimental description of the method and how it is applied to spam detection. A new detector model and matching rule model are also generated for effective matching of both self and non-self in other to burst the detector performance of the model. Our unique matching technique use in the negative selection algorithm help the model to overcome the limitation of a normal negative selection algorithm in defining harmfulness of self and non-self. This improves the requirement of the model and satisfactory requirement in terms of true positive and false positive rates. The experimental result confirms that the proposed model is able to establish a better true positive on an unknown spam