Kernel based intrusion detection system

Recently, applying artificial intelligence, machine learning and data mining techniques to intrusion detection system are increasing. But most of researches are focused on improving the performance of classifier. Selecting important features from input data lead to a simplification of the problem, faster and more accurate detection rates. Thus, selecting important features is an important issue in intrusion detection. Another issue in intrusion detection is that most of the intrusion detection systems are performed by off-line and it is not proper method for realtime intrusion detection system. In this paper, we develop the realtime intrusion detection system which combines on-line feature extraction method with least squares support vector machine classifier. Applying proposed system to KDD CUP 99 data, experimental results show that it has remarkable performance compared to off-line intrusion detection system.