False data injection attacks detection based on Laguerre function in nonlinear Cyber‐Physical systems

Li, Hongran; Xia, Yu; Ke, Jiacheng; Lv, Tieli; Zhang, Heng; Zhong, Zhaoman; Zhang, Jian

doi:10.1002/itl2.399

Cited by 4 publications

(3 citation statements)

References 13 publications

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“…The repressive attacks can destroy the integrity of the information by suppressing the transmission of information, leading to the failure of the UAV to fly normally or even crash, such as Denial of Service (DoS) attacks 5 and black hole attacks. Deceptive attacks often disrupt the availability of data and information through data tampering, such as false data injection (FDI) attacks 6 and reply attacks 7 . Once the transmission signal sent by GCS to the UAV is monitored and intercepted, the attack signals are designed by mimicking the transmission signals.…”

Section: Introductionmentioning

confidence: 99%

Adaptive observer‐based attack estimation and event‐triggered security control for UAVs

Pan

Lyu

Yang

2023

Adaptive Control & Signal

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Summary With the increasingly close connection between unmanned aerial vehicles (UAV) and communication networks, it is facing an increasing number of cybersecurity threats. In this article, an event‐triggered security control problem for UAVs is investigated under the tampered input signal sent by the ground control station. First, a linear model of the lateral movement of UAV is established and the attack description is given. Second, an adaptive observer is developed to reconstruct the attack signal. The reconstructed signal is compensated as system disturbance, and then the safety control strategy with event‐triggered mechanism (ETM) is designed. Furthermore, through the time interval analysis approach, the ETM, state observer, controller and attack signal are unified into a new augment system. Then, the uniformly ultimately bounded analysis and controller/observer synthesis conditions are obtained by using piecewise Lyapunov–Krasovskii functional methods. Furthermore, a sufficient condition for the parameters involving controller gain, observer gain, and the ETM parameters is given via a linear matrix inequality. Finally, simulation results are conducted to verify the effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Adaptive observer‐based attack estimation and event‐triggered security control for UAVs

Pan

Lyu

Yang

2023

Adaptive Control & Signal

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“…It is necessary to establish a data market to promote the flow of data among data owners and potential buyer (i.e., data transaction), where a reasonable data pricing model is one of the significant breaking points. However, there may be sensitive information contained in CPS data, 3 making privacy become a critical concern for data sellers. Thus, the privacy loss should be compensated in the data pricing model.…”

Section: Introductionmentioning

confidence: 99%

Data pricing with privacy loss compensation for cyber‐physical systems: A Stackelberg game based approach

Yang

Feng

Wang

et al. 2023

Internet Technology Letters

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The integration of sensors in cyber‐physical systems has given rise to data markets, where data owners can offer their sensing data for sale to potential buyer. However, determining the optimal data price in such markets is a complex issue, which demands a careful consideration of the interests of all parties involved, as well as the potential privacy loss for data sellers. By taking privacy loss into account, this paper proposes a fair compensation model for data sellers and formulates the pricing problem as a Stackelberg game. An automatic data pricing algorithm is developed to calculate the optimal price maximizing the joint benefits of the data sellers and the buyer where the privacy loss of the data sellers are compensated reasonably. Numerical simulations validate the effectiveness of the proposed pricing model in balancing benefits and privacy loss.

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“…In the dominion of cybersecurity, DL methods are utilized for analyzing system logs, network traffic, and user behaviours, discriminating anomalous patterns representative of different cyberattacks, namely denial-ofservice (Dos), malware propagation, and intrusion attempt attacks [8]. By encrypting contextual relationships and extracting deep-level features, these techniques improved the accuracy of attack detection, thus fortifying defence approaches and allowing fast responses to developing attacks [9]. The adaptability of DL methods for actively developing attack approaches location them as crucial tools in preventative and real-time identification, alleviating risks, and reinforcing the security condition of digital environments [10].…”

Section: Introductionmentioning

confidence: 99%

AI Driven False Data Injection Attack Recognition Approach for Cyber-Physical Systems in Smart Cities

Joshi,

Sinha,

Kundu

et al. 2023

Journal of Smart Internet of Things

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Cyber-physical systems (CPS) combine the typical power grid with recent communication and control technologies, generating new features for attacks. False data injection attacks (FDIA) contain maliciously injecting fabricated data as to the system measurements, capable of due to improper decisions and disruptions in power distribution. Identifying these attacks is vital for preserving the reliability and integrity of the power grid. Researchers in this domain utilize modern approaches namely machine learning (ML) and deep learning (DL) for detecting anomalous forms in the data that signify the existence of such attacks. By emerging accurate and effective detection approaches, this research purposes to improve the resilience of CPS and make sure of a secure and continuous power supply to consumers. This article presents an Improved Equilibrium Optimizer with Deep Learning Enabled False Data Injection Attack Recognition (IEODL-FDIAR) technique in a CPS platform. The main purpose of the IEODL-FDIAR technique is to enable FDIA attack detection and accomplishes security in the CPSS environment. In the presented IEODL-FDIAR technique, the IEO algorithm is used for the feature subset selection process. Moreover, the IEODL-FDIAR technique applies a stacked autoencoder (SAE) model for FDIA attack detection. Furthermore, the pelican optimization algorithm (POA) can be utilized for the optimum hyperparameter chosen for the SAE algorithm which in turn boosts the detection outcomes of the SAE model. To portray the better outcome of the IEODL-FDIAR system, a wide range of simulation analyses are executed. A wide comparison analysis described the improved results of the IEODL-FDIAR technique with existing DL models.

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False data injection attacks detection based on Laguerre function in nonlinear Cyber‐Physical systems

Cited by 4 publications

References 13 publications

Adaptive observer‐based attack estimation and event‐triggered security control for UAVs

Adaptive observer‐based attack estimation and event‐triggered security control for UAVs

Data pricing with privacy loss compensation for cyber‐physical systems: A Stackelberg game based approach

AI Driven False Data Injection Attack Recognition Approach for Cyber-Physical Systems in Smart Cities

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