A Time-Series Self-Supervised Learning Approach to Detection of Cyber-physical Attacks in Water Distribution Systems

Mahmood, Haitham and Wu, Wenyan and Gaber, Mohamed Medhat (2022) A Time-Series Self-Supervised Learning Approach to Detection of Cyber-physical Attacks in Water Distribution Systems. Energies, 15 (3). ISSN 1996-1073

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Abstract

Water Distribution System (WDS) threats have significantly grown following the Maroochyshire incident, as evidenced by proofed attacks on water premises. As a result, in addition to traditional solutions (e.g., data encryption and authentication), attack detection is being proposed in WDS to reduce disruption cases. The attack detection system must meet two critical requirements: high accuracy and near real-time detection. This drives us to propose a two-stage detection system that uses self-supervised and unsupervised algorithms to detect Cyber-Physical (CP) attacks. Stage 1 uses heuristic adaptive self-supervised algorithms to achieve near real-time decision-making and detection sensitivity of 66% utilizing Boss. Stage 2 attempts to validate the detection of attacks using an unsupervised algorithm to maintain a detection accuracy of 94% utilizing Isolation Forest. Both stages are examined against time granularity and are empirically analyzed against a variety of performance evaluation indicators. Our findings demonstrate that the algorithms in stage 1 are less favored than those in the literature, but their existence enables near real-time decision-making and detection reliability. In stage 2, the isolation Forest algorithm, in contrast, gives excellent accuracy. As a result, both stages can collaborate to maximize accuracy in a near real-time attack detection system.

Item Type:	Article
Identification Number:	10.3390/en15030914
Dates:	Date Event 21 January 2022 Accepted 27 January 2022 Published
Uncontrolled Keywords:	attack detection; self-supervised learning; water distribution system; data intelligence; industrial cyber-physical systems
Subjects:	CAH10 - engineering and technology > CAH10-01 - engineering > CAH10-01-08 - electrical and electronic engineering CAH11 - computing > CAH11-01 - computing > CAH11-01-02 - information technology CAH11 - computing > CAH11-01 - computing > CAH11-01-05 - artificial intelligence
Divisions:	Faculty of Computing, Engineering and the Built Environment > College of Engineering
Depositing User:	Wenyan Wu
Date Deposited:	28 Jan 2022 09:53
Last Modified:	20 Jun 2024 11:50
URI:	https://www.open-access.bcu.ac.uk/id/eprint/12698

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