Control Behavior Integrity for Distributed Cyber-Physical Systems

Adepu, Sridhar; Brasser, Ferdinand; García, Luis Antonio Ribot; Rodler, Michael; Davi, Lucas; Sadeghi, Ahmad‐Reza; Zonouz, Saman

doi:10.1109/iccps48487.2020.00011

Cited by 22 publications

(5 citation statements)

References 51 publications

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“…Its applicability and superiority have been demonstrated in a security web scenario with load balancing and a case study on an inter-domain routing application 1 . In future, we are planning to evaluate our framework in a realistic industrial control system with adaptive behaviors [20]- [23] by constructing the game in an automated way and supporting Architecture Description Interchange Language, such as acme [24].…”

Section: Discussionmentioning

confidence: 99%

System Component-Level Self-Adaptations for Security via Bayesian Games

Zhang

2021

2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)

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Security attacks present unique challenges to selfadaptive system design due to the adversarial nature of the environment. However, modeling the system as a single player, as done in prior works in security domain, is insufficient for the system under partial compromise and for the design of finegrained defensive strategies where the rest of the system with autonomy can cooperate to mitigate the impact of attacks. To deal with such issues, we propose a new self-adaptive framework incorporating Bayesian game and model the defender (i.e., the system) at the granularity of components in system architecture. The system architecture model is translated into a Bayesian multiplayer game, where each component is modeled as an independent player while security attacks are encoded as variant types for the components. The defensive strategy for the system is dynamically computed by solving the pure equilibrium to achieve the best possible system utility, improving the resiliency of the system against security attacks.

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Section: Discussionmentioning

confidence: 99%

System Component-Level Self-Adaptations for Security via Bayesian Games

Zhang

2021

2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)

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“…DAD is one such model-based approach. SCADMAN [7] provides a control code generation and verification mechanism to ensure the correctness of the behavior of individual PLCs. It was implemented and evaluated on the SWaT testbed.…”

Section: Design-based Approachmentioning

confidence: 99%

Fusing Design and Machine Learning for Anomaly Detection in Water Treatment Plants

Raman,

Mathur

2024

Electronics

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Accurate detection of process anomalies is crucial for maintaining reliable operations in critical infrastructures such as water treatment plants. Traditional methods for creating anomaly detection systems in these facilities typically focus on either design-based strategies, which encompass physical and engineering aspects, or on data-driven models that utilize machine learning to interpret complex data patterns. Challenges in creating these detectors arise from factors such as dynamic operating conditions, lack of design knowledge, and the complex interdependencies among heterogeneous components. This paper proposes a novel fusion detector that combines the strengths of both design-based and machine learning approaches for accurate detection of process anomalies. The proposed methodology was implemented in an operational secure water treatment (SWaT) testbed, and its performance evaluated during the Critical Infrastructure Security Showdown (CISS) 2022 event. A comparative analysis against four commercially available anomaly detection systems that participated in the CISS 2022 event revealed that our fusion detector successfully detected 19 out of 22 attacks, demonstrating high accuracy with a low rate of false positives.

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“…A security plan must be implemented to safeguard the intelligent grid [16]. The researchers in [17] proposed several monitoring procedures to keep an eye out for suspicious branch flow variations and anomalous load deviations to identify fake data injection (FDI) attacks. In this work [18], the authors proposed detecting FDI attacks using deep learning.…”

Section: Related Workmentioning

confidence: 99%

Ensemble Model Based on Hybrid Deep Learning for Intrusion Detection in Smart Grid Networks

AlHaddad,

Basuhail,

Khemakhem

et al. 2023

Sensors

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The Smart Grid aims to enhance the electric grid’s reliability, safety, and efficiency by utilizing digital information and control technologies. Real-time analysis and state estimation methods are crucial for ensuring proper control implementation. However, the reliance of Smart Grid systems on communication networks makes them vulnerable to cyberattacks, posing a significant risk to grid reliability. To mitigate such threats, efficient intrusion detection and prevention systems are essential. This paper proposes a hybrid deep-learning approach to detect distributed denial-of-service attacks on the Smart Grid’s communication infrastructure. Our method combines the convolutional neural network and recurrent gated unit algorithms. Two datasets were employed: The Intrusion Detection System dataset from the Canadian Institute for Cybersecurity and a custom dataset generated using the Omnet++ simulator. We also developed a real-time monitoring Kafka-based dashboard to facilitate attack surveillance and resilience. Experimental and simulation results demonstrate that our proposed approach achieves a high accuracy rate of 99.86%.

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Control Behavior Integrity for Distributed Cyber-Physical Systems

Cited by 22 publications

References 51 publications

System Component-Level Self-Adaptations for Security via Bayesian Games

System Component-Level Self-Adaptations for Security via Bayesian Games

Fusing Design and Machine Learning for Anomaly Detection in Water Treatment Plants

Ensemble Model Based on Hybrid Deep Learning for Intrusion Detection in Smart Grid Networks

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