Model and Algorithm in Artificial Immune System for Spam Detection

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