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dc.contributor.author Ramotsoela, DT
dc.contributor.author Hancke, GP
dc.contributor.author Abu-Mahfouz, Adnan MI
dc.date.accessioned 2019-09-26T10:55:48Z
dc.date.available 2019-09-26T10:55:48Z
dc.date.issued 2019-04
dc.identifier.citation Ramotsoela, D.T., Hancke, G.P. and Abu-Mahfouz, A.M.I. 2019. Attack detection in water distribution systems using machine learning. Human-centric Computing and Information Sciences, vol. 9(13): 1-22 en_US
dc.identifier.issn 2192-1962
dc.identifier.uri https://link.springer.com/article/10.1186/s13673-019-0175-8
dc.identifier.uri https://doi.org/10.1186/s13673-019-0175-8
dc.identifier.uri https://rdcu.be/bRWGz
dc.identifier.uri http://hdl.handle.net/10204/11138
dc.description Copyright: Springer 2019. 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://doi.org/10.1186/s13673-019-0175-8. A free fulltext non-print version of the article can be viewed at https://rdcu.be/bRWGz en_US
dc.description.abstract The threat to critical water system infrastructure has increased in recent years as is evident from the increasing number of reported attacks against these systems. Preventative security mechanisms are often not enough to keep attackers out so a second layer of security in the form of intrusion detection is paramount in order to limit the damage of successful attacks. In this paper several traditional anomaly detection techniques are evaluated in the context of attack detection in water distribution systems. These algorithms were centrally trained on the entire feature space and compared to multi-stage detection techniques that were designed to isolate both local and global anomalies. A novel ensemble technique that combines density-based and parametric algorithms was also developed and tested in the application environment. The traditional techniques had comparable results to the multi-stage systems and when used in conjunction with a local anomaly detector the performances of these algorithms were greatly improved. The developed ensemble technique also had promising results outperforming the density-based techniques and having comparable results to the parametric algorithms. en_US
dc.language.iso en en_US
dc.publisher Springer Nature en_US
dc.relation.ispartofseries Worklist;22666
dc.subject Anomaly detection en_US
dc.subject Critical infrastructure en_US
dc.subject Cyber-physical systems en_US
dc.subject Machine learning en_US
dc.subject System security en_US
dc.subject Water monitoring en_US
dc.title Attack detection in water distribution systems using machine learning en_US
dc.type Article en_US


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