A Network Intrusion Detection Model Using Fuzzy C4.5 Decision Tree

Abstract

With the growing rate of interconnections among computer systems, reliable network communication is becoming a major challenge. Intrusion detection has emerged as a significant field of research, because it is not theoretically possible to set up a system with no vulnerabilities. This paper purposes the use of fuzzy logic to generate decision tree to classify the intrusion data. Further, the fuzzy decision tree is then converted to fuzzy rules. The fuzzy decision tree (C4.5) method is used the minimize measure of classification ambiguity for different attributes. This method overcomes the sharp boundary problems; provide good accuracy dealing with continuous attributes and prediction problems. The experimental result is carried out by using 10% KDD Cup 99 benchmark network intrusion detection dataset.