Electronics Letters · 2014 · 68 citations · 4 references
ComputingComputer EngineeringComputer ScienceComputer SecurityCyber-physical SystemsNetwork SecurityCyber-physical SecurityIndustrial Control Systems SecurityIndustrial Network SecurityIntrusion AnalysisIntrusion DetectionAttack DetectionMachine Learning For Intrusion DetectionReal-time Intrusion DetectionScada SystemsSupervisory Control NetworksSupervisory Control SystemsSupervisory Control TheorySupport Vector Machine Classification
Intrusion detection in real‐time systems is a problem without a profound solution. In supervisory control and data acquisition (SCADA) systems the absence of a defence mechanism that can cope with different types of intrusions is of great importance. False positive alarms or mistakes regarding the origin of the intrusion mean severe costs for the system. An integrated one‐class support vector machine (OCSVM) mechanism that is distributed in a SCADA network is presented, as a part of an intrusion detection system, providing accurate information about the origin and the time of an intrusion. The module reads the network traffic, splits traffic according to the source of the packets and creates a cluster of OCSVM models. These trained models run in parallel and can accurately and fast recognise different types of attacks.
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Anomaly intrusion detection using one class SVM
Yanxin Wang, Johnny Wong, Andrew S. Miner · 2005 · 144 citations
Diversity measures for one-class classifier ensembles
Bartosz Krawczyk, Michał Woźniak · Neurocomputing · 2013 · 71 citations
Computing, Data Science, Data Mining +12