A novel mutual feature CRF for intrusion detection system

Intrusion detection system (IDS) is a system that monitors the network to find out the suspicious activity there by stopping the disruption that can be caused by intruders. Disruption caused by intruders can be stopped by having effective IDS. Intrusion detection system has three works, i.e., it continuously monitors network, compares with knowledge base and gives alarm to the administrator. The effectiveness of the system resides on the knowledge base. Preparing knowledge base is the most required part. This can be simply thought as a data analysis system. Data mining algorithms are applied to develop the knowledge base. Mining algorithm alone will not produce best knowledge base and so an additional step is required to sharp the algorithm. In this paper, we propose a mutual information feature selection algorithm with conditional random field, which produces high performance, less false positive IDS.