As smart grids expand in scale, connectivity, and intelligence, centralized state estimation can no longer meet the demands of complex coordination and robust operation. To address these challenges, this article proposes a distributed state estimation (DSE) solver based on a deep neural network, featuring an edge intelligence and collaborative computing architecture. First, a DSE model is developed based on the proposed dual-stream neural network, embedding node state ranges as constraints to improve cross-region learning of nonlinear measurement–state relationships. Second, during offline training, DSE training begins with intraregion local learning to capture regional characteristics and enhance estimation accuracy. It then proceeds to interregion collaborative estimation through supervised learning with round control, where a global zero vector-based state mapping method unifies partitioned measurement models and a joint loss enforces cross-region synchronization, ensuring boundary consistency and improving global robustness. During online operation, a timestamp-based delay-aware credible interpolation strategy is proposed to dynamically adjust the reliability of cross-region boundary information, while a joint loss function supports real-time fine-tuning to improve estimation accuracy and robustness. Finally, a DSE experimental platform is implemented on multiple ARM-based motherboards, and the effectiveness of the proposed solver is validated through comparative experiments on a partitioned IEEE 118-bus system.

While offloading computational tasks from the expanding array of intelligent power terminals in cyber-physical power system to untrusted edge servers relieves the central infrastructure, it also introduces a critical privacy risk to energy management data across the upload, computation, and distribution stages. A comprehensive privacy-preserving scheme for edge-computing-empowered power energy management system (EMS) is proposed, integrating blockchain and noise-enhanced elliptic curve cryptography (ECC) to establish a dual guarantee of data security in transmission and computational trustworthiness in processing. First, a privacy-preserving architecture for edge-computing-empowered EMS is proposed to protect the security of EMS data from every stage. Next, a Gaussian-noise-enhanced ECC-based encryption scheme is designed to protect the confidentiality and integrity of data during transmission, while reducing bandwidth consumption and improving encryption efficiency. Furthermore, a blockchain-based data computation privacy-preserving scheme is developed, with the EMS computation encapsulated within smart contracts, thereby ensuring the immutability of the algorithm and the privacy of the results even with third-party ES involvement. Finally, deployment on Hyperledger Fabric and IEEE 39-bus simulations validated the proposed EMS privacy-preserving scheme’s feasibility and effectiveness.

As data sharing is essential in applications such as distributed state estimation of cyber-physical power systems (CPPSs), the issue of data privacy leakage among individual regional system operators is increasingly concerned. To solve the issue, this paper proposes a new distributed security state estimation (DSSE) method based on homomorphic encryption for privacy-preserving consensus. First, considering that regional measurement data managed by individual regional system operators could be attacked by false data injection attacks (FDIAs), a new active attack detection method based on the statistical characteristics of the watermarking signal before and after FDIAs is proposed to improve detection proactivity and accuracy, and it is found that the detection accuracy is positively correlated with the watermarking intensity when the signal to interference plus noise ratio (SINR) is greater than 10db. Second, considering that each regional system operator needs to exchang intermediate data under the premise of protecting data privacy to guarantee the consistency process of distributed state estimation, a homomorphic encryption (HE)-based privacy-preserving consensus method is proposed, where a hash function-based dual verification mechanism is presented to prevent ciphertext data from being tampered by FDIAs. Third, according to the detection results and data compensation mechanism, a local secure state estimation model is proposed, and it is proved that the upper and lower bounds of the reconstructed estimation error covariance are not only related to system parameters and external noise but also negatively related to the compensation error. Furthermore, according to a Lyapunov function including privacy-preserving consensus, sufficient condition for consistency is proven, which depends on Laplacian matrix of the system and the iteration step size. Finally, experimental results demonstrate the feasibility and effectiveness of the proposed dynamic watermarking-based active attack detector and distributed secure state estimation method for CPPSs. Moreover, the computational overhead of incorporating advanced IND-CPA countermeasures (i.e., ciphertext re-randomization and branchless arithmetic) is quantified, which confirms the feasibility of practical deployment.

Multiplicative watermarking can enhance cyber attacks detection capacity of quantization-involved networked control system (QNCSs). This article presents an iteration-form solution to conventional summation-form multiplicative watermarking (SMW). First, limitation of conventional SMW for QNCSs is revealed, where there exists high design complexity whilst decreasing measurement perturbation from SMW and achieving successful replay attacks (RAs) detection. Second, a new iteration-form multiplicative watermarking (IMW) is designed by leveraging a keys transformation to reconstruct SMW with multiple coupled keys, where design complexity is greatly reduced and there are only dual decoupled keys to be designed. Furthermore, a positive correlation between value of one key and measurement perturbation from IMW is provided. Third, a positive correlation between ratio of the other key at different instants and RAs detection performance of IMW is explored by using residual covariance analysis. Finally, experimental results from networked inverted pendulum systems confirm the feasibility and effectiveness of new IMW.

Secure information exchange of the devices among different domains for cyber-physical power systems (CPPSs) is important yet challenging. Conventional blockchain-based authentication schemes generally adopt single blockchain and signature algorithm, only achieving intradomain or interdomain authentication with lower efficiency, and always failing to meet the confidentiality requirement during information interaction in CPPSs. To address these issues, this paper proposes a cross-domain authentication scheme based on distributed two-layer collaborative blockchains for CPPSs. First, a two-layer-blockchain collaborative authentication architecture is designed, deploying edge servers and taking into account the distributed characteristic of CPPSs. Second, a signcryption algorithm is developed by combining elliptic curve cryptography (ECC) with certificateless cryptography (CLC), which guarantees both the confidentiality and non-repudiation of the block information simultaneously. Furthermore, upper-layer alliance blockchain and lower-layer private blockchain are formed and interact collaboratively via index and Merkle proof, achieving intradomain and interdomain authentication with higher efficiency. Finally, a security analysis and experimental results are presented to superiorly demonstrate the security features and performance in comparison to other schemes in literature.

Most of the existing signcryption schemes utilize a key generation center to generate pseudonyms without updating, and usually opt for bilinear pairing to design authentication schemes. The disadvantage is that these schemes not only incur heavy computation and communication overheads during information interaction, but also can not eliminate security risks arising from not updating pseudonyms. These limitations render them less effective for smart terminals (STs) with limited computation and communication resources in cyber-physical power systems. The main purpose of this article is to explore a streamlined signcryption scheme tailored for resource-limited STs. To achieve this, a dynamical pseudonym self-generation mechanism (DPSGM) is first introduced to prevent the source from being linked and protect privacy. In addition, a streamlined signcryption scheme is designed based on elliptic curve cryptography and certificateless cryptography, integrating seamlessly with DPSGM. This design significantly reduces computation and communication burdens during information interaction. Finally, a real experimental platform is established to demonstrate the feasibility and effectiveness of the proposed scheme. Visual interfaces show the entire secure interaction process and the resistance to attacks.

The existing identity security schemes (e.g., based on bilinear pairing) have high computational complexity and large bytes of variables, which results in high computation and communication costs. It is difficult to apply the schemes to resource-constrained (i.e., computation and communication) devices. Moreover, most of these schemes adopt a fixed cycle key update strategy compromising the security of authentication schemes or dynamic (real-time) key update strategy with high computational cost. To solve these issues, this article explores a novel revocable lightweight authentication scheme for resource-constrained devices in cyber–physical power systems (CPPSs). First, a lightweight authentication scheme combined elliptic-curve cryptography (ECC) and certificateless cryptography (CLC) is proposed to negotiate a secure session key, which can achieve mutual authentication with low computation and communication costs. Second, aiming at security problem caused by key leakage, a real-time key update strategy with low computational cost is designed to improve the security of identity authentication. Third, according to a hardness assumption of the elliptic-curve discrete logarithm problem (ECDLP), theoretical analysis rigorously proves that the proposed authentication scheme ensures the security with respect to existential unforgeability against adaptively chosen message attacks (EUF-CMAs). Finally, experimental results confirm the feasibility and effectiveness of the proposed authentication scheme.

Dynamic watermarking schemes can enhance the cyberattack detection capability of networked control systems (NCSs). This article presents a linear event-triggered solution to conventional dynamic watermarking (CDW) schemes. First, the limitations of CDW schemes for event-triggered state estimation-based NCSs are investigated. Second, a new event-triggered dynamic watermarking (ETDW) scheme is designed by treating watermarking as symmetric key encryption, based on the limit convergence theorem in probability. Its security property against the generalized replay attacks (GRAs) is also discussed in the form of bounded asymptotic attack power. Third, finite sample ETDW tests are designed with matrix concentration inequalities. Finally, experimental results of a networked inverted pendulum system demonstrate the validity of our proposed scheme.

We here investigate the secure control of networked control systems developing a new dynamic watermarking (DW) scheme. First, the weaknesses of the conventional DW scheme are revealed, and the tradeoff between the effectiveness of false data injection attack (FDIA) detection and system performance loss is analyzed. Second, we propose a new DW scheme, and its attack detection capability is interrogated using the additive distortion power of a closed-loop system. Furthermore, the FDIA detection effectiveness of the closed-loop system is analyzed using auto/cross-covariance of the signals, where the positive correlation between the FDIA detection effectiveness and the watermarking intensity is measured. Third, the tolerance capacity of FDIA against the closed-loop system is investigated, and theoretical analysis shows that the system performance can be recovered from FDIA using our new DW scheme. Finally, the experimental results from a networked inverted pendulum system demonstrate the validity of our proposed scheme.

Aiming at the challenges of networked visual servo control systems, which rarely consider network communication duration and image processing computational cost simultaneously, we here propose a novel platform for networked inverted pendulum visual servo control using H∞ analysis. Unlike most of the existing methods that usually ignore computational costs involved in measuring, actuating, and controlling, we design a novel event-triggered sampling mechanism that applies a new closed-loop strategy to dealing with networked inverted pendulum visual servo systems of multiple time-varying delays and computational errors. Using the Lyapunov stability theory, we prove that the proposed system can achieve stability whilst compromising image-induced computational and network-induced delays and system performance. In the meantime, we use H∞disturbance attenuation level γ for evaluating the computational errors, whereas the corresponding H∞controller is implemented. Finally, simulation analysis and experimental results demonstrate the proposed system performance in reducing computational errors whilst maintaining system efficiency and robustness.

Smart grid (SG) represents a large-scale network system with the tight integration of a physical power network and an information network, which makes it more vulnerable to hybrid cyber attacks against different regional subsystems. First, an alternating direction method of multipliers-based distributed state estimation method is developed to overcome the limitation of conventional state estimation and performance analysis of SG against a single type of cyber attacks. Regional subsystems are partitioned via the K-means method. Second, a novel distributed state estimation method integrated with the characteristics of data deception attacks and denial of service (DoS) attacks is proposed to account for the simultaneous presence of different cyber attacks on individual regional subsystems. Third, the convergence of a distributed state estimation algorithm under hybrid cyber attacks is proved theoretically. Furthermore, the relationships between the convergence and algorithm parameters as well as the occurring probability of DoS attacks are established. Finally, the simulations on a modified IEEE 118-bus system are given to demonstrate the feasibility and effectiveness of the proposed method.

Currently cyber-security has attracted a lot of attention, in particular in wireless industrial control networks (WICNs). In this paper, the stability of wireless networked control systems (WNCSs) under deception attacks is studied with a token-based protocol applied to the data link layer (DLL) of WICNS. Since deception attacks cause the stability problem of WNCSs by changing the data transmitted over wireless network, it is important to detect deception attacks, discard the injected false data and compensate for the missing data (i.e., the discarded original data with the injected false data). The main contributions of this paper are: (1) With respect to the character of the token-based protocol, a switched system model is developed. Different from the traditional switched system where the number of subsystems is fixed, in our new model this number will be changed under deception attacks. (2) For this model, a new Kalman filter (KF) is developed for the purpose of attack detection and the missing data reconstruction. (3) For the given linear feedback WNCSs, when the noise level is below a threshold derived in this paper, the maximum allowable duration of deception attacks is obtained to maintain the exponential stability of the system. Finally, a numerical example based on a linearized model of an inverted pendulum is provided to demonstrate the proposed design.