An efficient intrusion detection system based on hypergraph - Genetic algorithm for parameter optimization and feature selection in support vector machine

• Published in: Knowledge-based systems Vol. 134; pp. 1 - 12
• Main Authors: Gauthama Raman, M.R., Somu, Nivethitha, Kirthivasan, Kannan, Liscano, Ramiro, Shankar Sriram, V.S.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 15.10.2017; Elsevier Science Ltd
• Subjects: Adaptive systems; Alarm systems; Artificial intelligence; Cybersecurity; Feature extraction; Feature subset; Genetic algorithm; Genetic algorithms; Graph theory; Hypergraph; Intrusion detection system; Intrusion detection systems; Kernel parameters; Machine learning; Optimization; Parameter optimization; Support vector machine; Support vector machines; Hypergraph; Support vector machine; Genetic algorithm; Intrusion detection system; Kernel parameters; Feature subset
• DOI: 10.1016/j.knosys.2017.07.005
• ISSN: 0950-7051

Realization of the importance for advanced tool and techniques to secure the network infrastructure from the security risks has led to the development of many machine learning based intrusion detection techniques. However, the benefits and limitations of these techniques make the development of an efficient Intrusion Detection System (IDS), an open challenge. This paper presents an adaptive, and a robust intrusion detection technique using Hypergraph based Genetic Algorithm (HG - GA) for parameter setting and feature selection in Support Vector Machine (SVM). Hyper – clique property of Hypergraph was exploited for the generation of initial population to fasten the search for the optimal solution and to prevent the trap at the local minima. HG-GA uses a weighted objective function to maintain the trade-off between maximizing the detection rate and minimizing the false alarm rate, along with the optimal number of features. The performance of HG-GA SVM was evaluated using NSL-KDD intrusion dataset under two scenarios (i) All features and (ii) informative features obtained from HG – GA. Experimental results show the prominence of HG-GA SVM over the existing techniques in terms of classifier accuracy, detection rate, false alarm rate, and runtime analysis.