Network intrusion detection by artificial immune system

With the increasing network attacks worldwide, intrusion detection (ID) has become a hot research topic in last decade. Technologies such as neural networks and fuzzy logic have been applied in ID. The results are varied. Intrusion detection accuracy is the main focus for intrusion detection systems (IDS). Most research activities in the area aim to improve the ID accuracy. In this paper, an artificial immune system (IMS) based network intrusion detection scheme is proposed. An optimized feature selection and parameter quantization algorithms are 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. In our experiments, a number of feature sets have been tried and compared. Compromise between complexity and detection accuracy has been discussed in the paper.