Model-based approach for cyber-physical attack detection in water distribution systems

2018 ◽  
Vol 139 ◽  
pp. 132-143 ◽  
Author(s):  
Mashor Housh ◽  
Ziv Ohar
Keyword(s):  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution Systems ◽  
Attack Detection ◽  
Model Based ◽  
Physical Attack

scholarly journals Cyber—Physical Attack Detection in Water Distribution Systems with Temporal Graph Convolutional Neural Networks

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1247
Author(s):  
Lydia Tsiami ◽  
Christos Makropoulos
Keyword(s):  
Neural Networks ◽  
Distribution System ◽  
Distribution Systems ◽  
Water Distribution ◽  
Attack Detection ◽  
Convolutional Network ◽  
Irreversible Damage ◽  
Physical Attack ◽  
Temporal Graph ◽  
Graph Neural Networks

Prompt detection of cyber–physical attacks (CPAs) on a water distribution system (WDS) is critical to avoid irreversible damage to the network infrastructure and disruption of water services. However, the complex interdependencies of the water network’s components make CPA detection challenging. To better capture the spatiotemporal dimensions of these interdependencies, we represented the WDS as a mathematical graph and approached the problem by utilizing graph neural networks. We presented an online, one-stage, prediction-based algorithm that implements the temporal graph convolutional network and makes use of the Mahalanobis distance. The algorithm exhibited strong detection performance and was capable of localizing the targeted network components for several benchmark attacks. We suggested that an important property of the proposed algorithm was its explainability, which allowed the extraction of useful information about how the model works and as such it is a step towards the creation of trustworthy AI algorithms for water applications. Additional insights into metrics commonly used to rank algorithm performance were also presented and discussed.

scholarly journals Contamination Event Detection in Water Distribution Systems Using a Model-based Approach

2014 ◽  
Vol 89 ◽  
pp. 1089-1096 ◽  
Author(s):  
D.G. Eliades ◽  
T.P. Lambrou ◽  
C.G. Panayiotou ◽  
M.M. Polycarpou
Keyword(s):  
Event Detection ◽  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution Systems ◽  
Model Based ◽  
Contamination Event

scholarly journals Review of model-based and data-driven approaches for leak detection and location in water distribution systems

2021 ◽  
Author(s):  
Zukang Hu ◽  
Beiqing Chen ◽  
Wenlong Chen ◽  
Debao Tan ◽  
Dingtao Shen
Keyword(s):  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution Systems ◽  
Leak Detection ◽  
Data Driven ◽  
Detection Methods ◽  
Water Utility ◽  
Model Based ◽  
Utility Companies ◽  
Leak Detection And Location

Abstract Leak detection and location in water distribution systems (WDSs) is of utmost importance for reducing water loss, which is, however, a major challenge for water utility companies. To this end, researchers have proposed a multitude of methods to detect such leaks in WDSs. Model-based and data-driven approaches, in particular, have found widespread uses in this area. In this paper, we reviewed both these approaches and classified the techniques used by them according to their leak detection methods. It is seen that model-based approaches require highly calibrated hydraulic models, and their accuracies are sensitive to modeling and measurement uncertainties. On the contrary, data-driven approaches do not require an in-depth understanding of the WDS. However, they tend to result in high false positive rates. Furthermore, neither of these approaches can handle anomalous variations caused by unexpected water demands.

Detecting Cyber-Physical Attacks in Water Distribution Systems: One-class Classifier Approach

2020 ◽  
Author(s):  
Mashor Housh ◽  
Noy Kadosh ◽  
Alex Frid
Keyword(s):  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution Systems ◽  
Attack Detection ◽  
Support Vector ◽  
Features Selection ◽  
Cyber Attack ◽  
Detection Problem ◽  
One Class Classification

<p>Water Distribution Systems (WDSs) are critical infrastructures that supply drinking water from water sources to end-users. Smart WDSs could be designed by integrating physical components (e.g. valve and pumps) with computation and networking devices. As such, in smart WDSs, pumps and valves are automatically controlled together with continuous monitoring of important systems' parameters. However, despite its advantage of improved efficacy, the automated control and operation through a cyber-layer can expose the system to cyber-physical attacks. One-Class classification technique is proposed to detect such attacks by analyzing collected sensors' readings from the system components. One-class classifiers have been found suitable for classifying "normal" and "abnormal" conditions with unbalanced datasets, which are expected in the cyber-attack detection problem. In the cyber-attack detection problem, typically, most of the data samples are under the "normal" state, and only small fraction of the samples can be suspected as under-attack (i.e. "abnormal" state). The results of this study demonstrate that one-class classification algorithms can be suitable for the cyber-attack detection problem and can compete with existing approaches. More specifically, this study examines the Support Vector Data Description (SVDD) method together with a tailored features selection methodology, which is based on the physical understanding of the WDS topology. The developed algorithm is examined on BATADAL datasets, which demonstrate a quasi-realistic case study and on a new case study of a large-scale WDS.</p>

scholarly journals Cyber-Attack Detection in Water Distribution Systems Based on Blind Sources Separation Technique

Water ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 795
Author(s):  
Bruno Brentan ◽  
Pedro Rezende ◽  
Daniel Barros ◽  
Gustavo Meirelles ◽  
Edevar Luvizotto ◽  
...  
Keyword(s):  
Remote Control ◽  
Distribution Systems ◽  
Water Distribution ◽  
Water Distribution Systems ◽  
Attack Detection ◽  
Cyber Attacks ◽  
Urban Systems ◽  
Cyber Attack ◽  
Statistical Control ◽  
Fast Independent Component Analysis

Service quality and efficiency of urban systems have been dramatically boosted by various high technologies for real-time monitoring and remote control, and have also gained privileged space in water distribution. Monitored hydraulic and quality parameters are crucial data for developing planning, operation and security analyses in water networks, which makes them increasingly reliable. However, devices for monitoring and remote control also increase the possibilities for failure and cyber-attacks in the systems, which can severely impair the system operation and, in extreme cases, collapse the service. This paper proposes an automatic two-step methodology for cyber-attack detection in water distribution systems. The first step is based on signal-processing theory, and applies a fast Independent Component Analysis (fastICA) algorithm to hydraulic time series (e.g., pressure, flow, and tank level), which separates them into independent components. These components are then processed by a statistical control algorithm for automatic detection of abrupt changes, from which attacks may be disclosed. The methodology is applied to the case study provided by the Battle of Attack Detection Algorithms (BATADAL) and the results are compared with seven other approaches, showing excellent results, which makes this methodology a reliable early-warning cyber-attack detection approach.

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