Mboweni, IVAbu-Mahfouz, Adnan MIRamotsoela, DT2022-01-242022-01-242021-10Mboweni, I., Abu-Mahfouz, A.M. & Ramotsoela, D. 2021. A machine learning approach to intrusion detection in water distribution systems – A review. http://hdl.handle.net/10204/12222 .978-1-6654-3554-32577-1647DOI: 10.1109/IECON48115.2021.9589237http://hdl.handle.net/10204/12222The confidentiality, integrity and availability of critical infrastructure is crucial for any economy to operate efficiently. Water distribution critical infrastructure is a target of many attackers who aim to penetrate the system for malicious reasons. The use of cyber-physical systems (CPSs) in Water Distribution Systems unveils many vulnerabilities that attackers can use. Although preventative security mechanisms are put into place they too can be defeated, and in this case, a second layer of security is essential. Intrusion detection mechanisms are important reactive security mechanisms to limit the damage done by a successful attack in the system. In this paper machine learning (ML) techniques for anomaly detection (AD) are reviewed.AbstractenAnomaly detectionCritical infrastructureCyber-physical systemsIntrusion detectionMachine learningConference PresentationMboweni, I., Abu-Mahfouz, A. M., & Ramotsoela, D. (2021). A machine learning approach to intrusion detection in water distribution systems – A review. http://hdl.handle.net/10204/12222Mboweni, IV, Adnan MI Abu-Mahfouz, and DT Ramotsoela. "A machine learning approach to intrusion detection in water distribution systems – A review." <i>The 47th Annual Conference of the IEEE Industrial Electronics Society (IECON), Toronto, Canada, 13-16 October 2021</i> (2021): http://hdl.handle.net/10204/12222Mboweni I, Abu-Mahfouz AM, Ramotsoela D, A machine learning approach to intrusion detection in water distribution systems – A review; 2021. http://hdl.handle.net/10204/12222 .TY - Conference Presentation AU - Mboweni, IV AU - Abu-Mahfouz, Adnan MI AU - Ramotsoela, DT AB - The confidentiality, integrity and availability of critical infrastructure is crucial for any economy to operate efficiently. Water distribution critical infrastructure is a target of many attackers who aim to penetrate the system for malicious reasons. The use of cyber-physical systems (CPSs) in Water Distribution Systems unveils many vulnerabilities that attackers can use. Although preventative security mechanisms are put into place they too can be defeated, and in this case, a second layer of security is essential. Intrusion detection mechanisms are important reactive security mechanisms to limit the damage done by a successful attack in the system. In this paper machine learning (ML) techniques for anomaly detection (AD) are reviewed. DA - 2021-10 DB - ResearchSpace DP - CSIR J1 - The 47th Annual Conference of the IEEE Industrial Electronics Society (IECON), Toronto, Canada, 13-16 October 2021 KW - Anomaly detection KW - Critical infrastructure KW - Cyber-physical systems KW - Intrusion detection KW - Machine learning LK - https://researchspace.csir.co.za PY - 2021 SM - 978-1-6654-3554-3 SM - 2577-1647 T1 - A machine learning approach to intrusion detection in water distribution systems – A review TI - A machine learning approach to intrusion detection in water distribution systems – A review UR - http://hdl.handle.net/10204/12222 ER -25206