E-mail Spam Classification With Artificial Neural Network and Negative Selection Algorithm

This paper apply neural network and spam model based on Negative selection algorithm for solving complex problems in spam detection. This is achieved by distinguishing spam from non-spam (self from non-self). We propose an optimized technique for e-mail classification; The e-mail are classified as self and non-self whose redundancy was removed from the detector set in the previous research to generate a self and non-self detector memory. A vector with an array of two element self and non-self concentration vector are generated into a feature vector used as an input in neural network classifier to classify the self and non-self feature vector of self and nonself program. The hybridization of both neural network and our previous model will further enhance our spam detector by improving the false rate and also enable the two different detectors to have a uniform platform for effective performance rate.