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

energies-15-00914.pdf - Published Version
Available under License Creative Commons Attribution.

Download (1MB)


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: https://doi.org/10.3390/en15030914
21 January 2022Accepted
27 January 2022Published
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 > School of Engineering and the Built Environment
Depositing User: Wenyan Wu
Date Deposited: 28 Jan 2022 09:53
Last Modified: 28 Jan 2022 09:53
URI: https://www.open-access.bcu.ac.uk/id/eprint/12698

Actions (login required)

View Item View Item


In this section...