Abstract
Recently machine learning-based Intrusion Detection systems (IDs) have been subjected to extensive researches because they can detect both misuse and anomaly. Most of existing IDs use all features in the network packet to look for known intrusive patterns. In this paper a new hybrid model RSC-PGP (Rough Set Classification - Parallel Genetic Programming) is presented to address the problem of identifying important features in building an intrusion detection system, increase the convergence speed and decrease the training time of RSC. Tests are done on KDD-99 data used for The Third International Knowledge Discovery and Data Mining Tools Competition. Results showed that the proposed model gives better and robust representation of rules as it was able to select features resulting in great data reduction, time reduction and error reduction in detecting new attacks. Empirical results reveal that Genetic Programming (GP) based techniques could play a major role in developing IDs which are light weight and accurate when compared to some of the conventional intrusion detection systems based on machine learning paradigms.