Mabunda, NRamotsoela, DTAbu-Mahfouz, Adnan MI2023-04-112023-04-112022-12Mabunda, N., Ramotsoela, D. & Abu Mahfouz, A.M. 2022. Intrusion detection in water distribution systems using machine learning techniques. http://hdl.handle.net/10204/12744 .978-1-6654-8240-0979-8-3503-9676-8DOI: 10.1109/ISIE51582.2022.9831687http://hdl.handle.net/10204/12744Water distribution systems/networks (WDS/WDN) use complex pipe networks to distribute water from reservoirs, tanks and rivers to consumers. Over the years, the water industry has deployed SCADA (Supervisory Control and Data Acquisition) systems into WDNs so that there is a uniform water balance and so that the demands of a fastgrowing world population are met optimally. These SCADA systems use standard protocols, hardware and software and thus are targeted due to their propensity to connect to institutional networks and the internet. Accurate and timeous detection of these attacks is necessary to protect critical infrastructure. Recently, Machine learning (ML) models have been initiated so that these cyber-attacks can be detected. These models can be categorized as Regression and prediction-based models, Classification-based models and Min-max based models. This paper will serve to cover the research gap in intrusion detection using machine learning, specifically in water distribution networks. This paper will aid in understanding which machine learning techniques are best suited for water distribution applications.AbstractenWDNSystems control and data acquisitionSCADALogistic regressionWater distribution networksConference PresentationMabunda, N., Ramotsoela, D., & Abu Mahfouz, A. M. (2022). Intrusion detection in water distribution systems using machine learning techniques. http://hdl.handle.net/10204/12744Mabunda, N, DT Ramotsoela, and Adnan MI Abu Mahfouz. "Intrusion detection in water distribution systems using machine learning techniques." <i>IEEE Second International Conference on Artificial Intelligence of Things, Istanbul, Turkey, 29-30 December 2022</i> (2022): http://hdl.handle.net/10204/12744Mabunda N, Ramotsoela D, Abu Mahfouz AM, Intrusion detection in water distribution systems using machine learning techniques; 2022. http://hdl.handle.net/10204/12744 .TY - Conference Presentation AU - Mabunda, N AU - Ramotsoela, DT AU - Abu Mahfouz, Adnan MI AB - Water distribution systems/networks (WDS/WDN) use complex pipe networks to distribute water from reservoirs, tanks and rivers to consumers. Over the years, the water industry has deployed SCADA (Supervisory Control and Data Acquisition) systems into WDNs so that there is a uniform water balance and so that the demands of a fastgrowing world population are met optimally. These SCADA systems use standard protocols, hardware and software and thus are targeted due to their propensity to connect to institutional networks and the internet. Accurate and timeous detection of these attacks is necessary to protect critical infrastructure. Recently, Machine learning (ML) models have been initiated so that these cyber-attacks can be detected. These models can be categorized as Regression and prediction-based models, Classification-based models and Min-max based models. This paper will serve to cover the research gap in intrusion detection using machine learning, specifically in water distribution networks. This paper will aid in understanding which machine learning techniques are best suited for water distribution applications. DA - 2022-12 DB - ResearchSpace DP - CSIR J1 - IEEE Second International Conference on Artificial Intelligence of Things, Istanbul, Turkey, 29-30 December 2022 KW - WDN KW - Systems control and data acquisition KW - SCADA KW - Logistic regression KW - Water distribution networks LK - https://researchspace.csir.co.za PY - 2022 SM - 978-1-6654-8240-0 T1 - Intrusion detection in water distribution systems using machine learning techniques TI - Intrusion detection in water distribution systems using machine learning techniques UR - http://hdl.handle.net/10204/12744 ER -26677