[논문]CRF Based Intrusion Detection System using Genetic Search Feature Selection for NSSA

Azhagiri M; Rajesh A; Rajesh P; Gowtham Sethupathi M

doi:10.22937/ijcsns.2023.23.7.15

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

International journal of computer science and network security : IJCSNS, v.23 no.7, 2023년, pp.131 - 140

Azhagiri M (Computer Science and Engineering, SRM Institute of Science and Technology) , Rajesh A (Computer Science and Engineering , C.Abdul Hakeem College of Engineering and Technology) , Rajesh P (Computer Science and Engineering , Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology) , Gowtham Sethupathi M (Computer Science and Engineering, SRM Institute of Science and Technology)

Abstract ▼ AI-Helper

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.

주제어

표/그림 (10)

그림 Fig. 1. Graphical Representation of Linear Chain CRF
표 Table 1: Features in the NSL-KDD Dataset
표 Table 2: Characteristics of the Sample KDD train+ Dataset used for the Experimentation
표 Table 3: Ranking of the Features of the KDDTrain+ dataset
표 Table 4. Detection Details of the Different Attack Categories of the Proposed System
표 Table 5. Classification Statistics of the Proposed System
표 Table 6. Precision, Recall and F-measure of the Proposed System
표 Table 7. Accuracy of the various IDSs
그림 Fig. 2. Performance Comparison of the various IDSs
표 Table 8. Performance Comparison of the various IDSs

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