Anomaly Detection in Networking Using Hybrid Artificial Immune Algorithm

Especially in today's network scenario, when computers are interconnected through internet, security of an information system is very important issue. Because no system can be absolutely secure, the timely and accurate detection of anomalies is necessary. The main aim of this research paper is to improve the anomaly detection by using Hybrid Artificial Immune Algorithm (HAIA) which is based on Artificial Immune Systems (AIS) and Genetic Algorithm (GA). In this research work, HAIA approach is used to develop Network Anomaly Detection System (NADS). The detector set is generated by using GA and the anomalies are identified using Negative Selection Algorithm (NSA) which is based on AIS. The HAIA algorithm is tested with KDD Cup 99 benchmark dataset. The detection rate is used to measure the effectiveness of the NADS. The results and consistency of the HAIA are compared with earlier approaches and the results are presented. The proposed algorithm gives best results when compared to the earlier approaches.