Intrusion Detection Using Rough Sets Based Parallel Genetic Algorithm Hybrid Model

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. Some of these features are irrelevant or redundant. Rough Set Classification (RSC), a modern learning algorithm, is used to rank features extracted for detecting intrusions and generate intrusion detection models. In this paper a new hybrid model RSC-PGA (Rough Set Classification Parallel Genetic Algorithm) 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 dataset 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. Keywords— Intrusion detection, Parallel genetic algorithm, Rough set classification.