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Title |
CRF Based Intrusion Detection System using Genetic Search Feature Selection for NSSA |
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Author |
Azhagiri M, Rajesh A, Rajesh P, and Gowtham Sethupathi M |
| Citation |
Vol. 23 No. 7 pp. 131-140 |
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Abstract |
Network security situational awareness systems helps in better managing the security concerns of a network, by monitoring for any anomalies in the network connections and recommending remedial 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 anomalies in the network. The conditional random fields being discriminative models are capable of directly modeling the conditional probabilities rather than joint probabilities there by achieving better classification accuracy. The genetic search feature selection algorithm is capable of identifying the optimal subset among the features based on the best population of features associated with the target class. The 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. |
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Keywords |
Network Security Situational Awareness (NSSA), Intrusion Detection System (IDS), Network Security, Intelligent Systems, Conditional Random Fields(CRF), Feature selection, Machine learning. |
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