Fusion-Based FDI Attack Detection in Cyber-Physical Systems

Gao, Lingjie; Chen, Bin

doi:10.1109/tcsii.2019.2939276

Cited by 38 publications

(8 citation statements)

References 25 publications

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“…For a ∆-M, it has been shown that the system is stable if |v k+1 | ≤ |v k | [32]. Let us investigate using (29), the behaviour of the trajectory for two possible cases:…”

Section: Remarkmentioning

confidence: 99%

“…This is a process of mapping a large set of input values to a small set of output values where the continuous-time signals are represented by quantised signals. In the past, different types of quantisers have been proposed by researchers which include nearest neighbour quantisers [26], [27], logarithmic quantisers [28], [29], neural network quantisers [30] and so on. Note that as the number of quantisation levels decreases, the effectiveness of the quantisers in bandwidth utilisation increases.…”

Section: Introductionmentioning

confidence: 99%

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Delta-Modulator-Based Quantised Output Feedback Controller for Linear Networked Control Systems

et al. 2020

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This paper proposes a ∆-Modulator (∆-M) based quantised output feedback controller for linear networked systems. The proposed ∆-M is essentially a 2-level quantiser, in contrast to some of the existing quantisers such as 2 p level (p ≥ 1) uniform-interval-nearest-neighbour quantiser, and offers various advantages which include lower design complexity, less noisy and lower cost. The three key components of the control system: the controller, the filter and the quantiser are designed to achieve the desired performance. The stability conditions of the ∆-M are derived and conditions for the existence of zigzag behaviour in steady-state are determined. The performance of the proposed controller is illustrated through simulations considering practical communication network based on ZigBee protocol. The results of the simulation demonstrate that the proposed controller could effectively achieve desired performance under various imperfections of the practical communication network. INDEX TERMS ∆-Modulation, Quantised Control, Networked Control Systems (NCS), Output Feedback Control.

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“…For a ∆-M, it has been shown that the system is stable if |v k+1 | ≤ |v k | [32]. Let us investigate using (29), the behaviour of the trajectory for two possible cases:…”

Section: Remarkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Delta-Modulator-Based Quantised Output Feedback Controller for Linear Networked Control Systems

et al. 2020

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“…As a new generation of industrial systems, cyber‐physical systems (CPSs) have efficiently integrated physical processes and cyber equipment with ubiquitous computation resources and communication capabilities. The increasingly open cyber environment promotes the rapid development of CPSs in terms of theoretical research and engineering applications, while also causes an increase in fault‐inducing factors and the possibility of malicious cyber attacks, for example, denial‐of‐service (DoS) attacks, 1 false data injection (FDI) attacks, 2 replay attacks, 3 and so on. Different from system faults 4 or unknown inputs, 5,6 the statistical characteristics or structure information of cyber attacks may be unknown, which may lead to catastrophic system failure and unbearable damage.…”

Section: Introductionmentioning

confidence: 99%

Secure estimation against non‐fixed channel attacks in cyber‐physical systems

Zhou

Chen

2022

Intl J Robust & Nonlinear

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This article studies the secure estimation problem for cyber-physical systems (CPSs) with sparse attacks and noises. Instead of fixed attacked sensor channels, the malicious attack investigated in this work is s-sparse and the set of attacked sensor channels is time-varying. A novel l 1 -minimization based on QR decomposition, rather than minimizing the row support of the attack matrix directly, is designed to reconstruct malicious attacks. Furthermore, considering that the presence of Gaussian noises may produce estimation errors, a secure conservative estimation method based on Kalman filter is proposed to estimate the consolidated signal consisting of sparse attacks and measurement noises. Moreover, it is proved that robustness of the proposed estimation algorithm can be guaranteed with the upper bound of estimation error covariance. An illustrative example is utilized to show the effectiveness of the proposed methods.

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“…The mechanism and method of FDIA under DC and AC models were studied, and an attack detection method based on the CNN-GRU hybrid model was proposed . The optimal fusion estimation method was designed by adding a compensation factor to study the detection of FDIA attack signal under CPPS (Gao et al, 2019). However, there is no explanation on how to choose the appropriate compensation factor.…”

Section: Introductionmentioning

confidence: 99%

False Data Injection Attack Detection in Power Systems Based on Cyber-Physical Attack Genes

Dong

et al. 2021

Front. Energy Res.

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In the process of the detection of a false data injection attack (FDIA) in power systems, there are problems of complex data features and low detection accuracy. From the perspective of the correlation and redundancy of the essential characteristics of the attack data, a detection method of the FDIA in smart grids based on cyber-physical genes is proposed. Firstly, the principle and characteristics of the FDIA are analyzed, and the concept of the cyber-physical FDIA gene is defined. Considering the non-functional dependency and nonlinear correlation of cyber-physical data in power systems, the optimal attack gene feature set of the maximum mutual information coefficient is selected. Secondly, an unsupervised pre-training encoder is set to extract the cyber-physical attack gene. Combined with the supervised fine-tuning classifier to train and update the network parameters, the FDIA detection model with stacked autoencoder network is constructed. Finally, a self-adaptive cuckoo search algorithm is designed to optimize the model parameters, and a novel attack detection method is proposed. The analysis of case studies shows that the proposed method can effectively improve the detection accuracy and effect of the FDIA on cyber-physical power systems.

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Fusion-Based FDI Attack Detection in Cyber-Physical Systems

Cited by 38 publications

References 25 publications

Delta-Modulator-Based Quantised Output Feedback Controller for Linear Networked Control Systems

Delta-Modulator-Based Quantised Output Feedback Controller for Linear Networked Control Systems

Secure estimation against non‐fixed channel attacks in cyber‐physical systems

False Data Injection Attack Detection in Power Systems Based on Cyber-Physical Attack Genes

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