A selective parameter-based evolutionary technique for network intrusion detection.

Network intrusion detection has remained a field of rigorous research over the past few years. Advances in computing performance, in terms of processing power and storage, have allowed the use of resource-intensive intelligent algorithms, to detect intrusive activities, in a timely manner. Genetic Algorithms have emerged as a powerful domain-independent technique to facilitate searching of the most effective set of rules, to differentiate between normal and anomalous network traffic. The scope of research for developing cutting-edge and effective GA-based intrusion detection systems, has rapidly expanded to keep pace with variant attack types, increasingly witnessed from the adversary class. In this paper, we propose a GA-based technique for effectively identifying network intrusion attempts, and clearly differentiating these from normal network traffic. The performance of the proposed scheme is studied and analyzed on the KDD-99 intrusion benchmark data set. We performed a simulation-based analysis of the proposed scheme, with results strengthening our findings, and providing us directions for future work.