Security and Communication Networks · 2015 · 63 citations · 20 references
ComputingComputer EngineeringComputer ScienceComputer SecurityMachine LearningNetwork SecurityAnomaly DetectionComputer IntrusionIndustrial Communication NetworksIndustrial Control Systems SecurityIndustrial Network SecurityIntrusion DetectionAnomaly-based DetectionMachine Learning For Intrusion DetectionNetwork Traffic Anomaly DetectionOne-class Support Vector MachinesParticle Swarm OptimizationReal-time Intrusion DetectionReal-time RequirementsSupport Vector Machine Classification
In order to detect abnormal communication behaviors efficiently in today's industrial control system, a new intrusion detection algorithm based on One-Class Support Vector Machine OCSVM is proposed in this paper. In this algorithm, a normal communication behavior model is established by using OCSVM, and the Particle Swarm Optimization algorithm is designed to optimize OCSVM model parameters. Furthermore, we adopt the normal Modbus function code sequence to train OCSVM model, and then use this model to detect abnormal Modbus TCP traffic. Our simulation results show that the proposed algorithm not only is efficient and reliable but also meets the real-time requirements of anomaly detection in industrial control system. Copyright © 2015 John Wiley & Sons, Ltd.
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Optimal feature selection for support vector machines
Minh Hoai Nguyen, Fernando De la Torre · Pattern Recognition · 2009 · 239 citations