Applying Variable Coe_cient functions to Self-Organizing Feature Maps for Network Intrusion Detection on the 1999 KDD Cup Dataset

Self-Organizing Feature Maps (SOFM's) can be a valuable element in a network intrusion detection system. When classification is performed on a segment of network tra_c, the usual method for class determination is selecting the class which has the smallest measurement of the Euclidean distance from the multi-dimensional network tra_c sample to the class’ multi-dimensional prototype. This minimum distance is calculated with equivalent weights for each dimension of data in the network tra_c sample. In this paper we explore the possibility of applying di_erent randomly generated weightings to each dimension of data in the network tra_c sample to increase positive classifications of the network sample data provided by the 1999 KDD Cup Dataset. We show that there is improvement, and recommend that further studies be done in choosing the right evolutionary functions to help modify the hotspots and achieve better results.