On-Line Cyber Attack Detection in Water Networks through State Forecasting and Control by Pattern Recognition
Abstract
Water distribution systems are critical infrastructures that can be the subject of various types of attacks. Concerns range from biological and chemical intrusions in pipelines to operational issues in water control assets. Cyber-attacks have also turned today into a crucial issue to consider. In highly automatized infrastructures, cyber-attacks can be considered as non-planned actions changing system operation to non-expected scenarios. They can potentially produce unavailability of enough water appropriated for public consumption or critical services such as firefighting. This work proposes to tackle the identification of cyber-attack scenarios through an early-alarm system able to recognize patterns corresponding to abnormal working conditions of the system. Firstly, an on-line forecasting model is developed that is based on the water system expected state regarding nodal pressures, tank water levels and control device flows. Then, an approach based on non-linear autoregressive networks with exogenous inputs (NARX) is proposed to take advantage of both their computational efficiency and the strong influence of the periodicity of the inputs under study. Finally, an analysis of abrupt change point, conducted in a time series composed by the differences between the observed measurements and the expected data, is built on top of the forecasting model.
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With the exception of Brentan et al. (2017) and Pasha et al. (2017), there appears to be a strong correlation between time-to-detection and classification score.
“With the exception of Brentan et al. (2017) and Pasha et al. (2017), there appears to be a strong correlation between these two metrics (see Fig -ure 3).”
From Battle of the Attack Detection Algorithms: Disclosing Cyber Attacks on Water Distribution Networks (2018) · cites this paper as Brentan et al. (2017)
Brentan et al. (2017) algorithm consistently and accurately detected most of the attacks, but failed to identify the last one.
“As shown in Figure 4(d), this algorithm was able to consistently and accurately detect most of the attacks, but it failed to identify the last one.”
From Battle of the Attack Detection Algorithms: Disclosing Cyber Attacks on Water Distribution Networks (2018) · cites this paper as Brentan et al. (2017)
The algorithm proposed by Brentan et al. (2017) can be regarded as a strong performer.
“With a value of S equal to 0.896, the algorithm proposed by Brentan et al. (2017) can also be regarded as a strong performer.”
From Battle of the Attack Detection Algorithms: Disclosing Cyber Attacks on Water Distribution Networks (2018) · cites this paper as Brentan 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 Brentan et al. (2017)
The algorithm proposed by Brentan et al. (2017) achieved a score S equal to 0.896.
“With a value of S equal to 0.896, the algorithm proposed by Brentan et al. (2017) can also be regarded as a strong performer.”
From Battle of the Attack Detection Algorithms: Disclosing Cyber Attacks on Water Distribution Networks (2018) · cites this paper as Brentan et al. (2017)
Cites
Smart Water Networks and Cyber Security
Rasekh, A., Hassanzadeh, A., Mulchandani, S., Modi, S., & Banks, M. K. (2016) · Journal of Water Resources Planning and Management · doi:10.1061/(asce)wr.1943-5452.0000646 · Cited by 87 · see citing works
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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
- Brentan, B. M., Campbell, E., Lima, G., Manzi, D., Ayala-Cabrera, D., Herrera, M., Montalvo, I., Izquierdo, J., & Luvizotto, E.
- Affiliations
- Universidade Estadual de Campinas (UNICAMP) (BR)
- Universidade Estadual de Campinas
- Venue
- World Environmental and Water Resources Congress 2017
- Publisher
- American Society of Civil Engineers
- Published
- 2017-05-18
- Pages
- 583-592
- OpenAlex
- W2615853694
- Cited by
- 24
- References
- 1
- Citations by year
- 2025: 1 · 2024: 3 · 2023: 3 · 2022: 6 · 2021: 4 · 2020: 3 · 2019: 1 · 2018: 3
- FWCI
- 5.80
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- World Environmental and Water Resources Congress (EWRI) (publishedVersion)
- Cambridge University Engineering Department Publications Database (submittedVersion)
- 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, NARX network, state forecasting, change point detection, nodal pressure, pattern recognition, critical infrastructure security
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