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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). The authors 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 non-self program.
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