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Behavioural intrusion detection in water distribution> systems using neural networks

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dc.contributor.author Ramotsoela, TD
dc.contributor.author Hancke, GP
dc.contributor.author Abu-Mahfouz, Adnan MI
dc.date.accessioned 2021-02-09T13:37:06Z
dc.date.available 2021-02-09T13:37:06Z
dc.date.issued 2020-10
dc.identifier.citation Ramotsoela, T., Hancke, G. & Abu-Mahfouz, A.M. 2020. Behavioural intrusion detection in water distribution systems using neural networks. IEEE Access, vol. 8: http://hdl.handle.net/10204/11749 en_ZA
dc.identifier.uri http://hdl.handle.net/10204/11749
dc.description.abstract There has been an increasing number of attacks against critical water system infrastructure in recent years. This is largely due to the fact that these systems are heavily dependent on computer networks meaning that an attacker can use conventional techniques to penetrate this network which would give them access to the supervisory control and data acquisition (SCADA) system. The devastating impact of a successful attack in these critical infrastructure applications could be long-lasting with major social and financial implications. Intrusion detection systems are deployed as a secondary defence mechanism in case an attacker is able to bypass the systems preventative security mechanisms. In this thesis, behavioural intrusion detection is addressed in the context of detecting cyber-attacks in water distribution systems. A comparative analysis of various predictive neural network architectures is conducted and from this a novel voting-based ensemble technique is presented. Finally an analysis of how this approach to behavioural intrusion detection can be enhanced by both univariate and multivariate outlier detection techniques It was found that multiple algorithms working together are able to counteract their limitation to produce a more robust algorithm with improved results. en_US
dc.format Full text en_US
dc.language.iso en en_US
dc.relation.uri 2169-3536 en_US
dc.relation.uri DOI: 10.1109/ACCESS.2020.3032251 en_US
dc.relation.uri https://ieeexplore.ieee.org/document/9229451 en_US
dc.relation.uri https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9229451 en_US
dc.source IEEE Access, vol. 8 en_US
dc.subject Anomaly detection en_US
dc.subject Cyber-physical security en_US
dc.subject Industrial control systems en_US
dc.subject Machine learning en_US
dc.subject Water distribution systems en_US
dc.title Behavioural intrusion detection in water distribution> systems using neural networks en_US
dc.type Article en_US
dc.description.pages 190403 - 190416 en_US
dc.description.note This work is licensed under a Creative Commons Attribution 4.0 License. en_US
dc.description.cluster Next Generation Enterprises & Institutions en_US
dc.description.impactarea EDTRC Management en_US
dc.identifier.apacitation Ramotsoela, T., Hancke, G., & Abu-Mahfouz, A. M. (2020). Behavioural intrusion detection in water distribution> systems using neural networks. <i>IEEE Access, vol. 8</i>, http://hdl.handle.net/10204/11749 en_ZA
dc.identifier.chicagocitation Ramotsoela, TD, GP Hancke, and Adnan MI Abu-Mahfouz "Behavioural intrusion detection in water distribution systems using neural networks." <i>IEEE Access, vol. 8</i> (2020) http://hdl.handle.net/10204/11749 en_ZA
dc.identifier.vancouvercitation Ramotsoela T, Hancke G, Abu-Mahfouz AM. Behavioural intrusion detection in water distribution systems using neural networks. IEEE Access, vol. 8. 2020; http://hdl.handle.net/10204/11749. en_ZA
dc.identifier.ris TY - Article AU - Ramotsoela, TD AU - Hancke, GP AU - Abu-Mahfouz, Adnan MI AB - There has been an increasing number of attacks against critical water system infrastructure in recent years. This is largely due to the fact that these systems are heavily dependent on computer networks meaning that an attacker can use conventional techniques to penetrate this network which would give them access to the supervisory control and data acquisition (SCADA) system. The devastating impact of a successful attack in these critical infrastructure applications could be long-lasting with major social and financial implications. Intrusion detection systems are deployed as a secondary defence mechanism in case an attacker is able to bypass the systems preventative security mechanisms. In this thesis, behavioural intrusion detection is addressed in the context of detecting cyber-attacks in water distribution systems. A comparative analysis of various predictive neural network architectures is conducted and from this a novel voting-based ensemble technique is presented. Finally an analysis of how this approach to behavioural intrusion detection can be enhanced by both univariate and multivariate outlier detection techniques It was found that multiple algorithms working together are able to counteract their limitation to produce a more robust algorithm with improved results. DA - 2020-10 DB - ResearchSpace DP - CSIR J1 - IEEE Access, vol. 8 KW - Anomaly detection KW - Cyber-physical security KW - Industrial control systems KW - Machine learning KW - Water distribution systems LK - https://researchspace.csir.co.za PY - 2020 T1 - Behavioural intrusion detection in water distribution> systems using neural networks TI - Behavioural intrusion detection in water distribution> systems using neural networks UR - http://hdl.handle.net/10204/11749 ER - en_ZA
dc.identifier.worklist 23976


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