Fuzzy Generalized Hebbian Algorithm for Large-Scale Intrusion Detection System

Abstract

The huge number of irrelevant and redundant data used in building intrusion detection systems (IDS) is one of the common issues in network intrusion detection systems. This paper proposed the use of Fuzzy Generalized Hebbian Algorithm as a novel data reduction method to overcome this problem of data redundancy in IDS. Two methods for dimensionality reduction (GHA and Fuzzy GHA) were used and compared in this study. This allowed retaining the most relevant traffic data information from the network. Furthermore, the K Nearest Neighbor algorithm was applied for the classification of the test connections into 2 categories (attack or normal). The investigations were carried out on the KDDCUP ‘99 dataset and the results showed the Fuzzy GHA method to perform better than GHA in the detection of both U2R and DoS attacks.