A Novel Intrusion Detection Mechanism for SCADA systems that Automatically Adapts to Changes in Network Topology
Authors
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 anIntrusion 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.
Related Project
FP7 CockpitCI: Cybersecurity on SCADA: risk prediction, analysis and reaction tools for Critical InfrastructuresJournal
EAI Transactions in Industrial Networks and Intelligent Systems (INISCOM 2016 Special Edition with extended papers), January 2017PDF File
DOI
Cited by
Year 2019 : 4 citations
Mohiuddin, Ghulam. (2019). Modeling Network Intrusion Detection System Using Feed-Forward Neural Network using UNSW-NB15 Dataset, in Proc of Smart Energy Grid Engineering, Canada,
L. Zhiqiang, G. Mohi-Ud-Din, L. Bing, L. Jianchao, Z. Ye and L. Zhijun, "Modeling Network Intrusion Detection System Using Feed-Forward Neural Network Using UNSW-NB15 Dataset," 2019 IEEE 7th International Conference on Smart Energy Grid Engineering (SEGE), Oshawa, ON, Canada, 2019, pp. 299-303. DOI: 10.1109/SEGE.2019.8859773
Yin, Xiaochun & Liu, Zeng & Nkenyereye, Lewis & Ndibanje, Bruce. (2019). Toward an Applied Cyber Security Solution in IoT-Based Smart Grids: An Intrusion Detection System Approach. Sensors. 19. 4952. 10.3390/s19224952.
Muna Al-Hawawreh, Elena Sitnikova, and Frank den Hartog. 2019. An Efficient Intrusion Detection Model for Edge System in Brownfield Industrial Internet of Things. In Proceedings of the 3rd International Conference on Big Data and Internet of Things (BDIOT 2019). ACM, New York, NY, USA, 83-87. DOI: https://doi.org/10.1145/3361758.3361762
Year 2018 : 2 citations
Ibrar Yaqoob, Ibrahim Abaker Targio Hashem, Arif Ahmed, S.M. Ahsan Kazmi, Choong Seon Hong, Internet of things forensics: Recent advances, taxonomy, requirements, and open challenges, Future Generation Computer Systems, 2018, ISSN 0167-739X, https://doi.org/10.1016/j.future.2018.09.058.
Al-Hawawreh, Muna & Moustafa, Nour & Sitnikova, Elena. (2018). Identification of malicious activities in industrial internet of things based on deep learning models. Journal of Information Security and Applications. 41. 10.1016/j.jisa.2018.05.002.
Year 2017 : 1 citations
M. Al-Zewairi, S. Almajali and A. Awajan, "Experimental Evaluation of a Multi-layer Feed-Forward Artificial Neural Network Classifier for Network Intrusion Detection System," 2017 International Conference on New Trends in Computing Sciences (ICTCS), Amman, 2017, pp. 167-172.