Email Spam Detection Generation Algorithm for Negative Selection Algorithm with Hamming Distance Partial Matching Rules

Abstract

Negative selection algorithms (NSAs) are inspired by artificial immune system. It creates techniques that aim at developing the immune based model. This is done by distinguishing self from non-self spam in the generation of detectors. In general, NSAs has an exponential run time. This research study the significance of time and accuracy for two commonly used matching rules. The hamming and r-chunk matching rules, based on different threshold values (r) for generating set of fixed number of detectors. The results show the differences between the mean values of time and accuracy for hamming and r-chunk matching rules. Statistical t test shows that the difference between hamming and r-chunk matching rule are insignificant for accuracy while it is significant for time.