Detecting new forms of network intrusion using genetic programming

How to find and detect novel or unknown network attacks is one of the most important objectives in current intrusion detection systems. In this paper, a rule evolution approach based on genetic programming (GP) for detecting novel attacks on network is presented and four genetic operators namely reproduction, mutation, crossover and dropping condition operators are used to evolve new rules. New rules are used to detect novel or known network attacks. A training and testing dataset proposed by DARPA is used to evolve and evaluate these new rules. The proof of concept implementation shows that the rule generated by GP has a low false positive rate (FPR), a low false negative rate (FNR) and a high rate of detecting unknown attacks. Moreover, the rule base composed of new rules has high detection rate (DR) with low false alarm rate (FAR).