A review of research works on supervised learning algorithms for SCADA intrusion detection and classification

Alimi, OAOuahada, KAbu-Mahfouz, Adnan MIRimer, SAlimi, KOA2022-01-242022-01-242021-08Alimi, O., Ouahada, K., Abu-Mahfouz, A.M., Rimer, S. & Alimi, K. 2021. A review of research works on supervised learning algorithms for SCADA intrusion detection and classification. Sustainability, 13(17). http://hdl.handle.net/10204/122382071-1050https://doi.org/10.3390/su13179597http://hdl.handle.net/10204/12238Supervisory Control and Data Acquisition (SCADA) systems play a significant role in providing remote access, monitoring and control of critical infrastructures (CIs) which includes electrical power systems, water distribution systems, nuclear power plants, etc. The growing interconnectivity, standardization of communication protocols and remote accessibility of modern SCADA systems have contributed massively to the exposure of SCADA systems and CIs to various forms of security challenges. Any form of intrusive action on the SCADA modules and communication networks can create devastating consequences on nations due to their strategic importance to CIs’ operations. Therefore, the prompt and efficient detection and classification of SCADA systems intrusions hold great importance for national CIs operational stability. Due to their well-recognized and documented efficiencies, several literature works have proposed numerous supervised learning techniques for SCADA intrusion detection and classification (IDC). This paper presents a critical review of recent studies whereby supervised learning techniques were modelled for SCADA intrusion solutions. The paper aims to contribute to the state-of-the-art, recognize critical open issues and offer ideas for future studies. The intention is to provide a research-based resource for researchers working on industrial control systems security. The analysis and comparison of different supervised learning techniques for SCADA IDC systems were critically reviewed, in terms of the methodologies, datasets and testbeds used, feature engineering and optimization mechanisms and classification procedures. Finally, we briefly summarized some suggestions and recommendations for future research works.FulltextenArtificial neural networksCritical infrastructuresIndustrial control systemsIntrusion detectionSupervised learningSupervisory Control and Data AcquisitionSCADASupport vector machineA review of research works on supervised learning algorithms for SCADA intrusion detection and classificationArticleAlimi, O., Ouahada, K., Abu-Mahfouz, A. M., Rimer, S., & Alimi, K. (2021). A review of research works on supervised learning algorithms for SCADA intrusion detection and classification. Sustainability, 13(17), http://hdl.handle.net/10204/12238Alimi, OA, K Ouahada, Adnan MI Abu-Mahfouz, S Rimer, and KOA Alimi "A review of research works on supervised learning algorithms for SCADA intrusion detection and classification." Sustainability, 13(17) (2021) http://hdl.handle.net/10204/12238Alimi O, Ouahada K, Abu-Mahfouz AM, Rimer S, Alimi K. A review of research works on supervised learning algorithms for SCADA intrusion detection and classification. Sustainability, 13(17). 2021; http://hdl.handle.net/10204/12238.TY - Article AU - Alimi, OA AU - Ouahada, K AU - Abu-Mahfouz, Adnan MI AU - Rimer, S AU - Alimi, KOA AB - Supervisory Control and Data Acquisition (SCADA) systems play a significant role in providing remote access, monitoring and control of critical infrastructures (CIs) which includes electrical power systems, water distribution systems, nuclear power plants, etc. The growing interconnectivity, standardization of communication protocols and remote accessibility of modern SCADA systems have contributed massively to the exposure of SCADA systems and CIs to various forms of security challenges. Any form of intrusive action on the SCADA modules and communication networks can create devastating consequences on nations due to their strategic importance to CIs’ operations. Therefore, the prompt and efficient detection and classification of SCADA systems intrusions hold great importance for national CIs operational stability. Due to their well-recognized and documented efficiencies, several literature works have proposed numerous supervised learning techniques for SCADA intrusion detection and classification (IDC). This paper presents a critical review of recent studies whereby supervised learning techniques were modelled for SCADA intrusion solutions. The paper aims to contribute to the state-of-the-art, recognize critical open issues and offer ideas for future studies. The intention is to provide a research-based resource for researchers working on industrial control systems security. The analysis and comparison of different supervised learning techniques for SCADA IDC systems were critically reviewed, in terms of the methodologies, datasets and testbeds used, feature engineering and optimization mechanisms and classification procedures. Finally, we briefly summarized some suggestions and recommendations for future research works. DA - 2021-08 DB - ResearchSpace DP - CSIR J1 - Sustainability, 13(17) KW - Artificial neural networks KW - Critical infrastructures KW - Industrial control systems KW - Intrusion detection KW - Supervised learning KW - Supervisory Control and Data Acquisition KW - SCADA KW - Support vector machine LK - https://researchspace.csir.co.za PY - 2021 SM - 2071-1050 T1 - A review of research works on supervised learning algorithms for SCADA intrusion detection and classification TI - A review of research works on supervised learning algorithms for SCADA intrusion detection and classification UR - http://hdl.handle.net/10204/12238 ER -25265