Feature Selection in Intrusion Detection System over Mobile Ad-hoc Network
As Mobile ad-hoc network (MANET) has become a very important technology the security problem, especially, intrusion detection technique research has attracted many people�s effort. MANET is more vulnerable than wired network and suffers intrusion like wired network. This paper investigated some intrusion detection techniques using machine learning and proposed a profile based neighbor monitoring intrusion detection method. Further analysis shows that the features collected by each node are too many for wireless devices with limited capacity. We apply Markov Blanket algorithm  to the feature selection of the intrusion detection method. Experimental studies have shown that Markov Blanket algorithm can decrease the number of features dramatically with very similar detection rate.