Meta Learning Intrusion Detection in Real Time Network

With the rapid increase in connectivity and accessibility of computer systems over the internet which has resulted in frequent opportunities for intrusions and attacks, intrusion detection on the network has become a crucial issue for computer system security. Methods based on hand-coded rule sets are laborous to build and not very reliable. This problem has led to an increasing interest in intrusion detection techniques based upon machine learning or data mining. However, traditional data mining based intrusion detection systems use single classifier in their detection engines. In this paper, we propose a meta learning based method for intrusion detection by MultiBoosting multi classifiers. MultiBoosting can form decision committees by combining AdaBoost with wagging. It is able to harness both AdaBoost’s high bias and variance reduction with wagging’s superior variance reduction. Experiments results show that MultiBoosting can improve the detection performance of state-of-art machine learning based intrusion detection techniques. Furthermore, we present a Symmetrical Uncertainty (SU) based method for reducing network connection features to make MultiBoosting more efficient in real-time network environment, in the meanwhile, keep the detection performance unundermined and in some cases, even further improved.