Detection of Cyber-Attacks to Water Systems through Machine-Learning-Based Anomaly Detection in SCADA Data
Abstract
Vulnerability of water infrastructure to cyber-attacks is proliferated as the operation of these systems is automated using networked systems. Reports of several cyber-attacks to water systems such as the 2006 Pennsylvania Water Company hack and the 2012 Tijuana River sewage spill has proven the water community concerns for the security of these systems. This study proposes a machine-learning-based anomaly detection methodology for early cyber-attack detection and warning that is tailored to water distributions systems security. Attacks are assumed to be manifested as man-induced anomalies in SCADA time series data that should be detected in a short time with low false negative/positive rates. The methodology is trained by and examined on the BATtle of the Attack Detection ALgorithms (BATADAL) dataset.
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
The score S for Chandy et al. (2017) is 0.802 and for Pasha et al. (2017) is 0.773.
“Their score S is equal to 0.802 and 0.773, respectively.”
From Battle of the Attack Detection Algorithms: Disclosing Cyber Attacks on Water Distribution Networks (2018) · cites this paper as Chandy et al. (2017)
The algorithm of Chandy et al. (2017) was over-sensitive, identifying most attack instances but issuing numerous false alarms.
“The algorithm of Chandy et al. (2017) turned out to be over-sensitive-meaning that it was able to identify most of the attack instances, but at the cost of issuing numerous false alarms.”
From Battle of the Attack Detection Algorithms: Disclosing Cyber Attacks on Water Distribution Networks (2018) · cites this paper as Chandy et al. (2017)
Cited by
Sorted by citations
Battle of the Attack Detection Algorithms: Disclosing Cyber Attacks on Water Distribution Networks
Taormina, R., Galelli, S., Tippenhauer, N. O., Salomons, E., Ostfeld, A., Eliades, D. G., Aghashahi, M., Sundararajan, R., Pourahmadi, M., Banks, M. K., et al. (2018) · Journal of Water Resources Planning and Management · doi:10.1061/(asce)wr.1943-5452.0000969 · Cited by 277 · see citing works
Self-citationKravchik, M., & Shabtai, A. (2022) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2021.3050101 · Cited by 224 · see citing works
- Authors
- Chandy, S. E., Rasekh, A., Barker, Z. A., Campbell, B., & Shafiee, M. E.
- Affiliations
- Sensus Inc.
- Venue
- World Environmental and Water Resources Congress 2017
- Publisher
- American Society of Civil Engineers
- Published
- 2017-05-18
- Pages
- 611-616
- OpenAlex
- W2614813818
- Cited by
- 23
- References
- 0
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 3 · 2023: 4 · 2022: 3 · 2021: 3 · 2020: 1 · 2019: 2 · 2018: 2
- FWCI
- 2.09
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- World Environmental and Water Resources Congress (EWRI) (publishedVersion)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Smart Grid Security and Resilience
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Engineering › Civil and Structural Engineering › Water Systems and Optimization
- Keywords
- cyberattack detection, water distribution systems, SCADA data, anomaly detection, water infrastructure security, early warning, time series
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