A novel soft computing model using adaptive neuro-fuzzy inference system for intrusion detection

The main purpose of this paper is to incorporate several soft computing techniques into the classifying system to detect and classify intrusions from normal behaviors based on the attack type in a computer network. Some soft computing paradigms such as neuro-fuzzy networks, fuzzy inference approach and genetic algorithms are investigated in this work. A set of neuro-fuzzy classifiers are used to perform an initial classification. The fuzzy inference system would then be based on the outputs of neuro-fuzzy classifiers, making decision of whether the current activity is normal or intrusive. As a final point, in order to attain the best result, a genetic algorithm optimizes the structure of the fuzzy decision engine. The experiments and evaluations of the proposed method were done with the KDD Cup 99 intrusion detection dataset.