An Improved Artificial Immune System-Based Network Intrusion Detection by Using Rough Set

With the increasing worldwide network attacks, intrusion detection (ID) has become a popular research topic in last decade. Several artificial intelligence techniques such as neural networks and fuzzy logic have been applied in ID. The results are varied. The intrusion detection accuracy is the main focus for intrusion detection systems (IDS). Most research activities in the area aiming to improve the ID accuracy. In this paper, an artificial immune system (AIS) based network intrusion detection scheme is proposed. An optimized feature selection using Rough Set (RS) theory is defined. The complexity issue is addressed in the design of the algorithms. The scheme is tested on the widely used KDD CUP 99 dataset. The result shows that the proposed scheme outperforms other schemes in detection accuracy.