A Hybrid Intrusion Detection System Using Hamming and MAXNET Neural Nets Using NDIS Dataset
In this paper, the authors present a model to improve the security of network system. Intrusion Detection System (IDS) detects suspected patterns of network traffic through analysis of those traffic packets. The major problems of existing models are recognition of new attacks, low accuracy, and detection time and system adaptability. In this paper, evolving intrusion detection system is constructed using hamming and MAXNET Neural Network for recognize attack class in the network traffic with and NDIS (tcpdump) capture dataset. The detection rate of 798 records is 100%, and 5208 records is 88.7%. The experimental results demonstrate that the designed model is promising in terms of accuracy and computational time of real word intrusion detection.