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

Haitham Mahmoud, Wenyan Wu and Mohamed Medhat Gaber
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Haitham Mahmoud: School of Engineering and Built Environment, Birmingham City University, Birmingham B4 7XG, UK
Wenyan Wu: School of Engineering and Built Environment, Birmingham City University, Birmingham B4 7XG, UK
Mohamed Medhat Gaber: School of Computing and Digital Technology, Birmingham City University, Birmingham B4 7XG, UK

Energies, 2022, vol. 15, issue 3, 1-18

Abstract: Water Distribution System (WDS) threats have significantly grown following the Maroochy shire 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.

Keywords: attack detection; self-supervised learning; water distribution system; data intelligence; industrial cyber-physical systems (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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