ResearchSpace

Intrusion detection in water distribution systems using machine learning techniques: A survey

Show simple item record

dc.contributor.author Mabunda, HD
dc.contributor.author Ramotsoela, DT
dc.contributor.author Abu-Mahfouz, Adnan MI
dc.date.accessioned 2022-11-06T19:27:16Z
dc.date.available 2022-11-06T19:27:16Z
dc.date.issued 2022-06
dc.identifier.citation Mabunda, H., Ramotsoela, D. & Abu-Mahfouz, A.M. 2022. Intrusion detection in water distribution systems using machine learning techniques: A survey. http://hdl.handle.net/10204/12512 . en_ZA
dc.identifier.isbn 978-1-6654-8240-0
dc.identifier.isbn 978-1-6654-8239-4
dc.identifier.isbn 978-1-6654-8241-7
dc.identifier.uri DOI: 10.1109/ISIE51582.2022.9831687
dc.identifier.uri http://hdl.handle.net/10204/12512
dc.description.abstract Water distribution systems (WDS) are designed to supply potable water to businesses, industries and people in any area or location. Cyber-Physical systems (CPS) are used in water distribution systems and come with aided benefits, however, these systems are exposed to intruders who attack these systems for their own personal gain or to sabotage the system. There are a number of different techniques which are available to stop intruders from penetrating these systems and this paper discussed different machine learning techniques that can detect anomalies and as a result stop any potential intrusion. en_US
dc.format Abstract en_US
dc.language.iso en en_US
dc.relation.uri https://ieeexplore.ieee.org/document/9831687 en_US
dc.source 2022 IEEE 31st International Symposium on Industrial Electronics (ISIE), Anchorage, Alaska, USA, 1-3 June 2022 en_US
dc.subject Cyber-physical systems en_US
dc.subject Machine learning en_US
dc.subject Water distribution systems en_US
dc.title Intrusion detection in water distribution systems using machine learning techniques: A survey en_US
dc.type Conference Presentation en_US
dc.description.pages 418-423 en_US
dc.description.note ©2022 IEEE. Due to copyright restrictions, the attached PDF file only contains the abstract of the full text item. For access to the full text item, please consult the publisher's website: https://ieeexplore.ieee.org/document/9831687 en_US
dc.description.cluster Next Generation Enterprises & Institutions en_US
dc.description.impactarea EDT4IR Management en_US
dc.identifier.apacitation Mabunda, H., Ramotsoela, D., & Abu-Mahfouz, A. M. (2022). Intrusion detection in water distribution systems using machine learning techniques: A survey. http://hdl.handle.net/10204/12512 en_ZA
dc.identifier.chicagocitation Mabunda, HD, DT Ramotsoela, and Adnan MI Abu-Mahfouz. "Intrusion detection in water distribution systems using machine learning techniques: A survey." <i>2022 IEEE 31st International Symposium on Industrial Electronics (ISIE), Anchorage, Alaska, USA, 1-3 June 2022</i> (2022): http://hdl.handle.net/10204/12512 en_ZA
dc.identifier.vancouvercitation Mabunda H, Ramotsoela D, Abu-Mahfouz AM, Intrusion detection in water distribution systems using machine learning techniques: A survey; 2022. http://hdl.handle.net/10204/12512 . en_ZA
dc.identifier.ris TY - Conference Presentation AU - Mabunda, HD AU - Ramotsoela, DT AU - Abu-Mahfouz, Adnan MI AB - Water distribution systems (WDS) are designed to supply potable water to businesses, industries and people in any area or location. Cyber-Physical systems (CPS) are used in water distribution systems and come with aided benefits, however, these systems are exposed to intruders who attack these systems for their own personal gain or to sabotage the system. There are a number of different techniques which are available to stop intruders from penetrating these systems and this paper discussed different machine learning techniques that can detect anomalies and as a result stop any potential intrusion. DA - 2022-06 DB - ResearchSpace DP - CSIR J1 - 2022 IEEE 31st International Symposium on Industrial Electronics (ISIE), Anchorage, Alaska, USA, 1-3 June 2022 KW - Cyber-physical systems KW - Machine learning KW - Water distribution systems LK - https://researchspace.csir.co.za PY - 2022 SM - 978-1-6654-8240-0 SM - 978-1-6654-8239-4 SM - 978-1-6654-8241-7 T1 - Intrusion detection in water distribution systems using machine learning techniques: A survey TI - Intrusion detection in water distribution systems using machine learning techniques: A survey UR - http://hdl.handle.net/10204/12512 ER - en_ZA
dc.identifier.worklist 26025 en_US


Files in this item

Thumbnail
Name: RS_26025_Intrusion ...
Size: 8.635Kb
Format: PDF
Description: Abstract
View/Open

This item appears in the following Collection(s)

Show simple item record