A Hybrid Multiobjective Evolutionary Algorithm for Anomaly Intrusion Detection

Intrusion detection systems (IDS) are network security tools that process local audit data or monitor network traffic to search for specific patterns or certain deviations from expected behavior. We use a multiobjective evolutionary algorithm which is hybridized with an Artificial Immune System as a method of anomaly-based IDS because of the similarity between the intrusion detection system architecture and the biological immune systems. In this study, we tested the improvements we made to jREMISA, a multiobjective evolutionary algorithm inspired artificial immune system, on the DARPA 1999 dataset and compared our results with others in literature. The almost 100% true positive rate and 0% false positive rate of our approach, under the given parameter settings and experimental conditions, shows that the improvements are successful as an anomaly-based IDS when compared with related studies.