Secure state estimation for Cyber Physical Systems with sparse malicious packet drops

Fiore, Gabriella; Chang, Young Hwan; Hu, Qie; Benedetto, Maria Domenica Di; Tomlin, Claire J.

doi:10.23919/acc.2017.7963229

Cited by 17 publications

(5 citation statements)

References 25 publications

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“…Towards that, Choo et al [111] proposed a wavelet-based approach that is able to identify malicious data injection (providing counteract), in wireless sensor networks, and distinguish them from faulty behaviours. Cyber security is also a concern of Fiore et al [112], who aim to ensure secure-state estimation of UAV systems under sparse attacks. Indeed, many situations can be regarded as satisfiability problems and solved, for instance with approaches based on SMT [113][114][115], which indicate many links with other research areas and might lead to a unified approach regarding system verification, security enforcement, and interaction handling.…”

Section: Discussion Regarding Formal Verification Of Cyber-physical Smentioning

confidence: 99%

Survey on automated symbolic verification and its application for synthesising cyber‐physical systems

Cordeiro

Filho

Bessa

2019

IET cyber-phys. syst.

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Section: Discussion Regarding Formal Verification Of Cyber-physical Smentioning

confidence: 99%

Survey on automated symbolic verification and its application for synthesising cyber‐physical systems

Cordeiro

Filho

Bessa

2019

IET cyber-phys. syst.

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“…While there are numerous works on the topic, most of them focus on attack-resilient algorithms that consider measurement noise [11], [12], time varying attack support [13], robustness considerations [14] and distributed case [15]. There are also numerous applications including power systems [16], UAVs [17], energy delivery systems [18], autonomous vehicles and networked systems. In contrast with work that utilizes prior information for resilient estimation [19], [20], our work, despite assuming the knowledge of location of ACEs nodes in the topology of the CPES does not assume any information acquired from such nodes.…”

Section: Introductionmentioning

confidence: 99%

Resilient Cyber-Physical Energy Systems Using Prior Information Based on Gaussian Process

Konstantinou

Anubi

2022

IEEE Trans. Ind. Inf.

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The power grid infrastructure is a large-scale, heterogeneous, and complex cyber-physical system which forms the lifeline of modern societies. The trend of tight coupling of physics, communication and computation in cyber-physical energy systems (CPES) is evident by the inclusion of numerous measurement sensors. This contributes to enhancing the monitoring and control functionalities of CPES. At the same time, the occurrence of adverse effects constitutes a vital dimension of CPES operation. Increasing the resilience of critical energy systems of key importance for safeguarding the national economy and security. This paper considers the problem of optimal estimation with sensing measurements subject to arbitrary corruption resulting from adverse effects. Such signals can cause false situation awareness and/or trigger a sequence of cascading effects leading to an ultimate system failure. We formulate the problem as a constrained optimization with additional prior information posed as a set inclusion constraint on the measurement vector. It is shown that if the prior set satisfies certain conditions, the resulting recovery error bound is improved. The approach demonstrates enhancement of the CPES resiliency by using the Gaussian process as the basis of a prior generative probabilistic regression model using historical data. The validation of the resiliency mechanism using prior information is performed using the New York Independent System Operator (NYISO) grid data, demonstrating 100% successful state recovery for up to 60% of CPES sensor failures.

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“…Following that, a number of papers have either modified or extended the framework to include measurement noise [16], [17], time varying attack support [18], robustness considerations [19] and distributed case [20]. There are also numerous applications including but not limited to; power systems [7], UAVS [18], [21], energy delivery systems [22], autonomous vehicles and networked systems.…”

Section: Introductionmentioning

confidence: 99%

Resilient Optimal Estimation Using Measurement Prior

Anubi,

Konstantinou,

Roberts

2019

Preprint

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This paper considers the problem of optimal estimation for linear system with the measurement vector subject to arbitrary corruption by an adversarial agent. This problem is relevant to cyber-physical systems where, due to the tight coupling of physics, communication and computation, a malicious agent is able to exploit multiple inherent vulnerabilities in order to inject stealthy signals into the measurement process. These malicious signals are calculated to serve the attack objectives of causing false situation awareness and/or triggering a sequence of cascading effects leading to an ultimate system failure. We assume that the attacker can only compromise a portion, but not all, of the measurement channels simultaneously. However, once a channel is compromised, the attacker is free to modify the corresponding measurement arbitrarily.Consequently, the problem is formulated as a compressive sensing problem with additional prior-information model. The prior-information considered is a set inclusion constraint on the measurement vector. It is shown that if the prior set satisfies certain conditions, the resulting recovery error bound is much stronger. The approach is applied to the problem of resilient sate estimation of a power system. For this application, Gaussian Process is used to build a prior generative probabilistic regression model from historical data. The resulting Gaussian Process Regression model recursively maps energy market information to iid Gaussian distributions on the relevant system measurements. An optimization-based resilient state estimator is then developed using a re-weighted 1-minimization scheme. The developed algorithm is evaluated through a numerical simulation example of the IEEE 14-bus system mapped to the New York Independent System Operator (NYISO) grid data.

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Secure state estimation for Cyber Physical Systems with sparse malicious packet drops

Cited by 17 publications

References 25 publications

Survey on automated symbolic verification and its application for synthesising cyber‐physical systems

Survey on automated symbolic verification and its application for synthesising cyber‐physical systems

Resilient Cyber-Physical Energy Systems Using Prior Information Based on Gaussian Process

Resilient Optimal Estimation Using Measurement Prior

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