Detection of Cyber Physical Attacks on Water Distribution Systems via Principal Component Analysis and Artificial Neural Networks

Abokifa, Ahmed A.; Haddad, Kelsey; Lo, Cynthia S.; Biswas, Pratim

doi:10.1061/9780784480625.063

Cited by 30 publications

(40 citation statements)

References 8 publications

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“…Another work that achieved a high score in the competition is [39] in which the authors proposed a three-layer method, where the first layer detects statistical anomalies, the second layer is a neural network aimed at finding contextual inconsistencies with normal operation, and the third layer uses principal component analysis (PCA) on all sensor data to classify the samples as normal or abnormal. Our work differs from [39] in the following ways. First, we study the efficiency of a single generic mechanism, as opposed to the multilevel system used by [39].…”

Section: Related Workmentioning

confidence: 99%

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Detecting Cyber Attacks in Industrial Control Systems Using Convolutional Neural Networks

Kravchik

Shabtai

2018

Proceedings of the 2018 Workshop on Cyber-Physical Systems Security and PrivaCy

240

167

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Industrial control systems (ICSs) are widely used and vital to industry and society. Their failure can have severe impact on both economics and human life. Hence, these systems have become an attractive target for attacks, both physical and cyber. A number of attack detection methods have been proposed, however they are characterized by a low detection rate, a substantial false positive rate, or are system specific. In this paper, we study an attack detection method based on simple and lightweight neural networks, namely, 1D convolutions and autoencoders. We apply these networks to both the time and frequency domains of the collected data and discuss pros and cons of each approach. We evaluate the suggested method on three popular public datasets and achieve detection rates matching or exceeding previously published detection results, while featuring small footprint, short training and detection times, and generality. We also demonstrate the effectiveness of PCA, which, given proper data preprocessing and feature selection, can provide high attack detection scores in many settings. Finally, we study the proposed method's robustness against adversarial attacks, that exploit inherent blind spots of neural networks to evade detection while achieving their intended physical effect. Our results show that the proposed method is robust to such evasion attacks: in order to evade detection, the attacker is forced to sacrifice the desired physical impact on the system. This finding suggests that neural networks trained under the constraints of the laws of physics can be trusted more than networks trained under more flexible conditions.

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Section: Related Workmentioning

confidence: 99%

“…First, we study the efficiency of a single generic mechanism, as opposed to the multilevel system used by [39]. Second, our solution evaluates types of neural networks not covered by [39]. In addition, we study frequency domain anomaly detection and adversarial robustness.…”

Section: Related Workmentioning

confidence: 99%

Detecting Cyber Attacks in Industrial Control Systems Using Convolutional Neural Networks

Kravchik

Shabtai

2018

Proceedings of the 2018 Workshop on Cyber-Physical Systems Security and PrivaCy

240

167

View full text Add to dashboard Cite

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“…Taormina and Galelli [81,82] used autoencoders (deep neural networks) in detecting attacks. Abokifa et al [83,84] proposed a detection approach composed of three layers to detect anomalies in the BATADAL datasets; first removing outliers using statistical analysis then, using a feed forward artificial neural network (ANN), a multilayer perceptron (MLP) to identify anomalies and, finally, principal component analysis (PCA) to identify multiple affected sensors. Giacomoni et al [85] developed two detection approaches based on data-mining.…”

Section: Cyber-attack Detection Modelsmentioning

confidence: 99%

A Systematic Review of the State of Cyber-Security in Water Systems

Tuptuk

Hazell

Watson

et al. 2021

Water

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Critical infrastructure systems are evolving from isolated bespoke systems to those that use general-purpose computing hosts, IoT sensors, edge computing, wireless networks and artificial intelligence. Although this move improves sensing and control capacity and gives better integration with business requirements, it also increases the scope for attack from malicious entities that intend to conduct industrial espionage and sabotage against these systems. In this paper, we review the state of the cyber-security research that is focused on improving the security of the water supply and wastewater collection and treatment systems that form part of the critical national infrastructure. We cover the publication statistics of the research in this area, the aspects of security being addressed, and future work required to achieve better cyber-security for water systems.

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“…Seven teams [28][29][30][31][32][33][34] took part in the competition and all of the teams but two proposed multi-stage detection algorithms. One of the teams that didn't produced a model based technique that simulated the hydraulic processes using EPANET and used the error between the expected and actual values to determine whether or not an attack had occurred [28].…”

Section: The Battle Of the Attack Detection Algorithmsmentioning

confidence: 99%

Attack detection in water distribution systems using machine learning

Ramotsoela

Hancke

Abu‐Mahfouz

2019

Hum. Cent. Comput. Inf. Sci.

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The increased popularity of cyber physical systems (CPS) in recent years is evident in the variety of different applications where they are deployed [1-5]. Water distribution systems (WDS) have also embraced the convenience and flexibility of these CPS where digital computation and networking form an integral part of the monitoring and control of physical processes. The deployment of these smart water networks is paramount in the development of the envisioned smart cities of the future [6]. The added water management functionality introduced by CPS corresponds with an increase of vulnerability to cyber-attacks from intruders with malicious intent. This is largely due to the fact that these systems are heavily dependent on computer networks meaning that an attacker can use conventional techniques to penetrate this network which would give them access to the supervisory control and data acquisition (SCADA) system [7]. Adequately securing smart city applications will be paramount in order for them to become a reality in future [8].

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Detection of Cyber Physical Attacks on Water Distribution Systems via Principal Component Analysis and Artificial Neural Networks

Cited by 30 publications

References 8 publications

Detecting Cyber Attacks in Industrial Control Systems Using Convolutional Neural Networks

Detecting Cyber Attacks in Industrial Control Systems Using Convolutional Neural Networks

A Systematic Review of the State of Cyber-Security in Water Systems

Attack detection in water distribution systems using machine learning

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