Water Distribution Systems Analysis Symposium–Battle of the Attack Detection Algorithms (BATADAL)

Aghashahi, Mohsen; Sundararajan, Raanju R.; Pourahmadi, Mohsen; Banks, M. Katherine

doi:10.1061/9780784480595.010

Cited by 15 publications

(8 citation statements)

References 6 publications

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“…The second uses these events along with raw data to train a deep learning model, a convolutional variational autoencoder, to detect attacks. Aghashahi et al [91] first extracted features related to the characteristics of the attack and no-attack data by using a covariance matrix and distance measure of every data point. Then, a random forest classifier was used to classify these characteristics as attack and normal operation.…”

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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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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“…A two-stage method based on feature vector extraction and classification was proposed in [12]: vector extraction was applied to multidimensional hydraulic data, and safety classification was performed by random forests, the machine-learning algorithm developed by [13]. In [14] recurrent neural networks (RNNs) were used for hydraulic state estimation of network district metered areas and, based on the RNN output, a statistical control process was applied for detecting abrupt changes in the residual time series.…”

Section: Related Workmentioning

confidence: 99%

“…FP and FN are the numbers of false positive and false negative time stamps. Criteria (9) and (12) are considered by [7] and the final score S is calculated as a weighted sum of S TTD and SCR (13)…”

Section: Performance Evaluationmentioning

confidence: 99%

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

Brentan

Rezende

Barros

et al. 2021

Water

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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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“…Traditional anomaly detection techniques [24][25][26][27][28] combined with densitybased and parametric algorithms [29] The use of multi-stage techniques that isolates both local and global anomalies will generally yield better anomaly detection results. The proposed approach outperforms the density-based techniques and has comparable results to the parametric algorithms.…”

Section: Approach Advantages Shortcomingsmentioning

confidence: 99%

CLAP: A Cross-Layer Analytic Platform for the Correlation of Cyber and Physical Security Events Affecting Water Critical Infrastructures

González-Granadillo

Díaz

Caubet

et al. 2021

JCP

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Water CIs are exposed to a wide number of IT challenges that go from the cooperation and alignment between physical and cyber security teams to the proliferation of new vulnerabilities and complex cyber-attacks with potential disastrous consequences. Although novel and powerful solutions are proposed in the literature, most of them lack appropriate mechanisms to detect cyber and physical attacks in real time. We propose a Cross-Layer Analytic Platform (denoted as CLAP) developed for the correlation of Cyber and Physical security events affecting water CIs. CLAP aims to improve the detection of complex attack scenarios in real time based on the correlation of cyber and physical security events. The platform assigns appropriate severity values to each correlated alarm that will guide security analysts in the decision-making process of prioritizing mitigation actions. A series of passive and active attack scenarios against the target infrastructure are presented at the end of the paper to show the mechanisms used for the detection and correlation of cyber–physical security events. Results show promising benefits in the improvement of response accuracy, false rates reduction and real-time detection of complex attacks based on cross-correlation rules.

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Water Distribution Systems Analysis Symposium–Battle of the Attack Detection Algorithms (BATADAL)

Cited by 15 publications

References 6 publications

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

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

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

CLAP: A Cross-Layer Analytic Platform for the Correlation of Cyber and Physical Security Events Affecting Water Critical Infrastructures

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