Research Article
A Novel Intrusion Detection Mechanism for SCADA systems which Automatically Adapts to Network Topology Changes
@ARTICLE{10.4108/eai.1-2-2017.152155, author={Barnaby Stewart and Luis Rosa and Leandros A. Maglaras and Tiago J. Cruz and Mohamed Amine Ferrag and Paulo Simoes and Helge Janicke}, title={A Novel Intrusion Detection Mechanism for SCADA systems which Automatically Adapts to Network Topology Changes}, journal={EAI Endorsed Transactions on Industrial Networks and Intelligent Systems}, volume={4}, number={10}, publisher={EAI}, journal_a={INIS}, year={2017}, month={2}, keywords={Intrusion Detection Systems, Support Vector Machines, Adaptive Mechanisms}, doi={10.4108/eai.1-2-2017.152155} }- Barnaby Stewart
Luis Rosa
Leandros A. Maglaras
Tiago J. Cruz
Mohamed Amine Ferrag
Paulo Simoes
Helge Janicke
Year: 2017
A Novel Intrusion Detection Mechanism for SCADA systems which Automatically Adapts to Network Topology Changes
INIS
EAI
DOI: 10.4108/eai.1-2-2017.152155
Abstract
Industrial Control Systems (ICS) are getting more vulnerable as they become increasingly interconnected with other systems. Industrial Internet of Things(IIoT) will bring new opportunities to business and society, along with new threats and security risks. One major change that ICS will face will be that of the dynamic network topology. Changes in the network architecture will affect the performance of the ICS along with the efficiency of the security mechanisms that are deployed. The current article investigates how changes in the network architecture of a supervisory control and data acquisition (SCADA) system affect the performance of an Intrusion Detection System IDS that is based on the One class Support Vector Machine (OCSVM). Also the article proposes an adaptive mechanism that can cope with such changes and can work in real time situations. The performance of the proposed adaptive IDS is tested using traces from a Hybrid ICS testbed with a dynamic topology.
Copyright © 2017 Barnaby Stewart et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.