Minimax control for cyber-physical systems under network packet scheduling attacks

Shoukry, Yasser; Araújo, José; Tabuada, Paulo; Srivastava, Mani; Johansson, Karl Henrik

doi:10.1145/2461446.2461460

Cited by 34 publications

(36 citation statements)

References 17 publications

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“…to controllers and from controllers to actuators, as shown in Fig. 1 and similarly considered in [10]. Each controller (implemented as a control task) collects the sensed information, processes it on a shared computation unit (e.g., a single-core CPU), 1 and sends commands to various actuators.…”

Section: General Codesign Frameworkmentioning

confidence: 99%

“…In the literature for cyber-physical system security, researchers have proposed various control-oriented approaches for attacks on cyber-physical systems [7]- [10]. Pasqualetti et al [7] designed attack detection and identification monitors from a control-theoretic perspective.…”

mentioning

confidence: 99%

“…In [9], a recursive networked predictive control method is proposed to deal with denial of service attacks. In [10], a minimax control approach is presented to address network packet scheduling attacks. Resource and real-time constraints are not considered in these approaches, and there is no guarantee of schedulability and control performance.…”

mentioning

confidence: 99%

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Cross-Layer Codesign for Secure Cyber-Physical Systems

Zheng

Deng

Anguluri

et al. 2016

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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Abstract-Security attacks may have disruptive consequences on cyber-physical systems, and lead to significant social and economic losses. Building secure cyber-physical systems is particularly challenging due to the variety of attack surfaces from the cyber and physical components, and often to limited computation and communication resources. In this paper, we propose a crosslayer design framework for resource-constrained cyber-physical systems. The framework combines control-theoretic methods at the functional layer and cybersecurity techniques at the embedded platform layer, and addresses security together with other design metrics such as control performance under resource and real-time constraints. We use the concept of interface variables to capture the interactions between control and platform layers, and quantitatively model the relation among system security, performance, and schedulability via interface variables. The general codesign framework is customized and refined to the automotive domain, and its effectiveness is demonstrated through an industrial case study and a set of synthetic examples.

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Section: General Codesign Frameworkmentioning

confidence: 99%

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confidence: 99%

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Cross-Layer Codesign for Secure Cyber-Physical Systems

Zheng

Deng

Anguluri

et al. 2016

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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“…Recent high-profile attacks, e.g., the Maroochy water This work was partially supported by the ONR under grants N00014-14-1-0554 and N00014-10-10952 and by the NSF under grant NSF CNS-1035588. Some material from this paper was presented at the 17th International Conference on Hybrid Systems: Computation and Control (HSCC 2014) and at the 53rd IEEE Conference on Decision and Control (CDC 2014 breach [29] and the control system malware Stuxnet [12], highlight the need to understand system security [23], [27], [30]. In [27], a packet delay attack is considered, in which, by accessing and then delaying the sensor data, the adversary can harm the physical components of the system.…”

Section: Introductionmentioning

confidence: 99%

“…Some material from this paper was presented at the 17th International Conference on Hybrid Systems: Computation and Control (HSCC 2014) and at the 53rd IEEE Conference on Decision and Control (CDC 2014 breach [29] and the control system malware Stuxnet [12], highlight the need to understand system security [23], [27], [30]. In [27], a packet delay attack is considered, in which, by accessing and then delaying the sensor data, the adversary can harm the physical components of the system. In [23] and [30], more general issues such as detectability and identifiability for a wide range of attacks are investigated.…”

Section: Introductionmentioning

confidence: 99%

Temporal Logics for Learning and Detection of Anomalous Behavior

Kong

Jones

Belta

2017

IEEE Trans. Automat. Contr.

105

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Abstract-The increased complexity of modern systems necessitates automated anomaly detection methods to detect possible anomalous behavior determined by malfunctions or external attacks. We present formal methods for inferring (via supervised learning) and detecting (via unsupervised learning) anomalous behavior. Our procedures use data to construct a signal temporal logic (STL) formula that describes normal system behavior. This logic can be used to formulate properties such as "If the train brakes within 500 m of the platform at a speed of 50 km/hr, then it will stop in at least 30 s and at most 50 s." Our procedure infers not only the physical parameters involved in the formula (e.g., 500 m in the example above) but also its logical structure. STL gives a more human-readable representation of behavior than classifiers represented as surfaces in high-dimensional feature spaces. The learned formula enables us to perform early detection by using monitoring techniques and anomaly mitigation by using formal synthesis techniques. We demonstrate the power of our methods with examples of naval surveillance and a train braking system.

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