Research of Network Intrusion Detection System based on Machine Learning and Rough Set Theory

Data mining for intrusion detection is one of the most cutting-edge researches which focus on network security, database, and information decision-making. Due to the emergence of new forms of attacks and intrusion on the network, we need a new intrusion detection system which would be able to detect new and unknown attacks. In the paper, by studying the characteristics of network data intrusion, we put forward a intrusion detection system based on Rough set theory, and detect anomaly action in network. This method can extract detection rule model of the network connection data, dealing with incomplete data and basic discrete data exit in data mining effectively. The experiments results show that, models, methods and generation framework proposed in this paper can effectively detect network intrusion.