Identification of Cyber Attacks on Water Distribution Systems by Unveiling Low-Dimensionality in the Sensory Data
Abstract
Modern water distribution networks (WDN) are evolving into cyber-physical systems comprising physical components, such as valves and pumps, and a cyber-layer of computing, sensing and communication devices. It is recognized that these cyber-components are vulnerable to malicious attacks that can range from sensor data manipulation to alteration of the control laws that govern the operation of physical components. This paper presents a data-driven, model-free optimization-based solution to the BATtle of the Attack Detection Algorithms (BATADAL) competition that aims to investigate various approaches for the detection of cyber-attacks in WDNs. Leveraging machine learning and system identification, this paper develops algorithms for detection of cyber-attacks on WDNs. The methodology relies on unveiling low dimensionality components in the sensory data as well as on the sparse nature of anomalies a natural property of cyber-attacks against large volumes of data. The chief computational instrument is convex optimization, for which computationally efficient solvers are available. The proposed methods are tested on simulated cyber-attacks from the C-Town network in EPANET and the provided data-sets.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Giacomoni et al. (2017) algorithm is less sensitive, resulting in a higher number of false negatives and minor timing errors, leading to a score S equal to 0.927.
“However, Giacomoni et al. (2017) algorithm is less sensitive, resulting in a higher number of false negatives and minor timing errors (see Figure 4(c)) that lead to a score S equal to 0.927.”
From Battle of the Attack Detection Algorithms: Disclosing Cyber Attacks on Water Distribution Networks (2018) · cites this paper as Giacomoni et al. (2017)
The algorithm proposed by Giacomoni et al. (2017) has the same number of false positives and true negatives as that of Housh and Ohar (2017b), meaning that both algorithms were the most successful in avoiding false alarms.
“The algorithm proposed by Giacomoni et al. (2017) has the same number of false positives and true negatives as that of Housh and Ohar (2017b)-meaning that both algorithms were the most successful in avoiding false alarms.”
From Battle of the Attack Detection Algorithms: Disclosing Cyber Attacks on Water Distribution Networks (2018) · cites this paper as Giacomoni et al. (2017)
Among algorithms that provided information on attacked devices, those proposed by Brentan et al. (2017) and Giacomoni et al. (2017) were the most accurate.
“Among these, the algorithms proposed by Brentan et al. (2017) and Giacomoni et al. (2017) were the most accurate.”
From Battle of the Attack Detection Algorithms: Disclosing Cyber Attacks on Water Distribution Networks (2018) · cites this paper as Giacomoni 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-citation
- Authors
- Giacomoni, M., Gatsis, N., & Taha, A.
- Affiliations
- The University of Texas at San Antonio (US)
- Venue
- World Environmental and Water Resources Congress 2017
- Publisher
- American Society of Civil Engineers
- Published
- 2017-05-18
- Pages
- 660-675
- OpenAlex
- W2614794881
- Cited by
- 27
- References
- 0
- Citations by year
- 2025: 4 · 2024: 3 · 2023: 4 · 2022: 6 · 2021: 3 · 2020: 2 · 2019: 2 · 2018: 3
- FWCI
- 5.80
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- World Environmental and Water Resources Congress (EWRI) (publishedVersion)
- Topics
- Physical Sciences › Engineering › Civil and Structural Engineering › Water Systems and Optimization
- Physical Sciences › Engineering › Control and Systems Engineering › Smart Grid Security and Resilience
- Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
- Keywords
- cyberattack detection, water distribution systems, cyber-physical systems, sensor data manipulation, low-dimensional subspace, convex optimization, EPANET, model-free detection
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
