An enhanced Hybrid Anomaly-based Detection Approach

During the last decade, Intrusion Detection Systems (IDSs) have played an important role in defending critical computer systems and networks from cyber-attacks. Anomaly detection techniques have received a particularly great amount of attention because they offer intrinsic ability to detect unknown attacks. In this paper, we propose an enhanced hybrid anomaly detection approach based on negative selection algorithm and metaheuristics. The enhancements include tuning some of its parameters value automatically without predefining them. NSL-KDD dataset; which is a modified version of the widely used KDDCUP99 dataset; is used for performance evaluation. KDDCUP99 dataset is criticized by its inability to reflected recent network traffic behaviour. So, a real time experiment was performed to capture and construct a recent dataset to ensure the performance of the proposed enhancements. Performance evaluation shows that the proposed approach outperforms other competitors of machine learning algorithms on both datasets.