CRF Based Intrusion Detection System Using Genetic Search Feature Selection for NSSA

Abstract

Abstract - \u00a0Network security situational awareness systems helps in better managing the security concerns of \u00a0a \u00a0network, \u00a0by \u00a0monitoring \u00a0for \u00a0any \u00a0anomalies \u00a0in \u00a0the \u00a0network \u00a0connections \u00a0and \u00a0recommending \u00a0remedial actions upon detecting an attack. An Intrusion Detection System helps in identifying the security concerns of a network, by monitoring for any anomalies in the network connections. We have proposed a CRF based IDS system using genetic search feature selection algorithm for network security situational awareness to detect any \u00a0anomalies \u00a0in \u00a0the \u00a0network. \u00a0The \u00a0conditional \u00a0random \u00a0fields \u00a0being \u00a0discriminative \u00a0models \u00a0are \u00a0capable \u00a0of directly \u00a0modeling \u00a0the \u00a0conditional \u00a0probabilities \u00a0rather \u00a0than \u00a0joint \u00a0probabilities \u00a0there \u00a0by \u00a0achieving \u00a0better classification accuracy. The genetic search feature selection algorithm is capable of identifying the optimal subset \u00a0among \u00a0the \u00a0features \u00a0based \u00a0on \u00a0the \u00a0best \u00a0population \u00a0of \u00a0features \u00a0associated \u00a0with \u00a0the \u00a0target \u00a0class. \u00a0The proposed system, when trained and tested on the bench mark NSL-KDD dataset exhibited higher accuracy in identifying an attack and also classifying the attack category.