Adaptive Security Control Against False Data Injection Attacks in Cyber-Physical Systems

Miao, Baoling; Wang, Hao; Liu, Yan-Jun; Liu, Lei

doi:10.1109/jetcas.2023.3253483

Cited by 9 publications

(7 citation statements)

References 40 publications

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“…to predict the sensor data, and then the predicted data are compared to the actual data [8]. Some attempts have been made to build predictive models to detect FDIAs through the dynamical equations of smart grids [9,10], unmanned aerial vehicles [11], water distribution systems [12], and cyber-physical systems [13][14][15]. However, this detection approach requires appropriate predictive models for specific domains and relies on a priori knowledge of specific physical behaviors, which allows for limited scalability.…”

Section: Fdia Detection Methodsmentioning

confidence: 99%

A Framework for Detecting False Data Injection Attacks in Large-Scale Wireless Sensor Networks

Hu,

Yang,

Yang

2024

Sensors

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False data injection attacks (FDIAs) on sensor networks involve injecting deceptive or malicious data into the sensor readings that cause decision-makers to make incorrect decisions, leading to serious consequences. With the ever-increasing volume of data in large-scale sensor networks, detecting FDIAs in large-scale sensor networks becomes more challenging. In this paper, we propose a framework for the distributed detection of FDIAs in large-scale sensor networks. By extracting the spatiotemporal correlation information from sensor data, the large-scale sensors are categorized into multiple correlation groups. Within each correlation group, an autoregressive integrated moving average (ARIMA) is built to learn the temporal correlation of cross-correlation, and a consistency criterion is established to identify abnormal sensor nodes. The effectiveness of the proposed detection framework is validated based on a real dataset from the U.S. smart grid and simulated under both the simple FDIA and the stealthy FDIA strategies.

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Section: Fdia Detection Methodsmentioning

confidence: 99%

A Framework for Detecting False Data Injection Attacks in Large-Scale Wireless Sensor Networks

Hu,

Yang,

Yang

2024

Sensors

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“…Due to limited energy, the attacker cannot carry out attacks arbitrarily, so ψ a and ϖ are bounded. [12,13,23] ϱ = 1 time-varying (6) in [15,19] ϱ is time-varying and satisfies 0 < ϱ min ≤ ϱ ≤ ϱmax time-varying (6) ϱ ̸ = 0 is a constant time-varying…”

Section: System Description and Control Objectivementioning

confidence: 99%

“…For linear CPS under sensor attacks, the adaptive security control strategy was first proposed in [11]. Afterward, researchers began to study the security control problems of nonlinear CPS [12][13][14][15][16][17][18]. In [15], both deception attacks and injection attacks were considered for a second-order nonlinear system.…”

Section: Introductionmentioning

confidence: 99%

“…Especially, by employing the Nussbaum function, reference [24] solves the issue of unknown control directions stemming from sensor and actuator attacks. However, it should be noted that the control schemes mentioned in [12][13][14][15][16][17][18][21][22][23][24] require that the model be strict-feedback nonlinear systems; namely, these control schemes could not be directly applied to non-strict feedback nonlinear stochastic systems subjected to sensor and actuator attacks.…”

Section: Introductionmentioning

confidence: 99%

“…3. Unlike the CPS deterministic/stochastic systems models in [12][13][14][15][16][17][18][22][23][24], the considered model is in a non-strict feedback form. Besides, some challenges caused by unknown network attacks and control directions are overcome by using only one Nussbaum gain function in controller design.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Output Feedback Adaptive Inverse OptimalSecurity Control for Stochastic NonlinearCyber-Physical Systems under Sensor andActuator Attacks

Chen,

Yu,

2024

Preprint

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This paper addresses the inverse optimal security control problems for a class of stochastic non-strict feedback nonlinear Cyber-Physical Systems under sensor and actuator attacks. The concerned system model includes both stochastic disturbances and more general nonlinearity. First, to make the control design feasible, a linear state transformation is applied to the attacked system. Furthermore, in the process of backstepping design, based on the Nussbaum gain function formula, fuzzy logic system approximation method, and inverse optimal control theory, combing the available output signal, an output feedback inverse optimal controller is proposed. Specifically, the designed controller not only ensures that the system is secure under network attacks but also optimal in terms of the cost function. Finally, two physical examples are given to verify the effectiveness of the proposed control scheme in various network attacks.

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Event‐triggered adaptive secure lateral stabilization for autonomous vehicles under actuator attacks

Sun,

Chen,

Shen

et al. 2024

Adaptive Control & Signal

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SummaryFalse data injection attacks can disrupt the steering control actions and make a real threat to both the security and safety of autonomous vehicles. In this paper, a secure event‐triggered lateral control approach of autonomous vehicles subject to actuator attacks is investigated. Firstly, an arbitrary unknown actuator attacks is considered in the secure lateral steering control of autonomous vehicles. Thus, to save communication resources for the limited bandwidth CAN bus, the periodic event‐triggered transmission scheme is utilized, transforming the established lateral steering control into a time‐delay system through the consideration of periodic event‐triggered sampling. Then, an adaptive control compensation scheme is developed to mitigate the malicious effects caused by actuator attacks. The proposed secure control approach is skilled in compensating the unknown attacked steering control actions in an adaptive way. The stabilization criteria under the adaptive secure control law is well derived by Lyapunov–Krasovskii method and some linear inequality matrices operations. At last, the effectiveness of the proposed secure control scheme is verified by some numerical experiments borrowed from a practical vehicle.

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Adaptive Security Control Against False Data Injection Attacks in Cyber-Physical Systems

Cited by 9 publications

References 40 publications

A Framework for Detecting False Data Injection Attacks in Large-Scale Wireless Sensor Networks

A Framework for Detecting False Data Injection Attacks in Large-Scale Wireless Sensor Networks

Output Feedback Adaptive Inverse OptimalSecurity Control for Stochastic NonlinearCyber-Physical Systems under Sensor andActuator Attacks

Event‐triggered adaptive secure lateral stabilization for autonomous vehicles under actuator attacks

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