The rapid development of information and communications technology has enabled the use of digital-controlled and software-driven distributed energy resources (DERs) to improve the flexibility and efficiency of power supply, and support grid operations. However, this evolution also exposes geographically-dispersed DERs to cyber threats, including hardware and software vulnerabilities, communication issues, and personnel errors, etc. Therefore, enhancing the cyber-resiliency of DER-based smart grid - the ability to survive successful cyber intrusions - is becoming increasingly vital and has garnered significant attention from both industry and academia. In this survey, we aim to provide a comprehensive review regarding the cyber-resiliency enhancement (CRE) developments of the DER-based smart grid, present a holistic CRE framework, and thoroughly discuss the research directions of the next-generation CRE methods. Firstly, an integrated threat modeling method is tailored for the hierarchical DER-based smart grid with special emphasises on vulnerability identification and impact analysis. Then, the defense-in-depth strategies encompassing prevention, detection, mitigation, and recovery are comprehensively surveyed, systematically classified, and rigorously summarized. A holistic CRE framework is subsequently proposed to incorporate the five key resiliency enablers. Finally, challenges and future directions are discussed in details. The overall aim of this survey is to illustrates the recent development of CRE methods and motivate further efforts to improve the cyber-resiliency of DER-based smart grid.

Machine learning (ML) sees an increasing prevalence of being used in the Internet of Things (IoT)-based smart grid. However, the trustworthiness of ML is a severe issue that must be addressed to accommodate the trend of ML-based smart grid applications (MLsgAPPs). The adversarial distortion injected into the power signal will greatly affect the system’s normal control and operation. Therefore, it is imperative to conduct vulnerability assessment for MLsgAPPs applied in the safety-critical power systems. In this article, we provide a comprehensive review of the recent progress in designing attack and defense methods for MLsgAPPs. Unlike the traditional survey about ML security, this is the first review work about the security of MLsgAPPs that focuses on the characteristics of power systems. We first highlight the specifics for constructing adversarial attacks on MLsgAPPs. Then, the vulnerability of MLsgAPP is analyzed from the perspective of the power system and ML model, respectively. Afterward, a comprehensive survey is conducted to review and compare existing studies about the adversarial attacks on MLsgAPPs in scenarios of generation, transmission, distribution, and consumption, and the countermeasures are reviewed according to the attacks that they defend against. Finally, the future research directions are discussed on the attacker’s and defender’s side, respectively. We also analyze the potential vulnerability of large language model-based (e.g., ChatGPT) smart grid applications. Overall, our purpose is to encourage more researchers to contribute to investigating the adversarial issues of MLsgAPPs.

Recent studies have considered thwarting false data injection (FDI) attacks against state estimation in power grids by proactively perturbing branch susceptances. This approach is known as moving target defense (MTD). However, despite of the deployment of MTD, it is still possible for the attacker to launch stealthy FDI attacks generated with former branch susceptances. In this paper, we prove that, an MTD has the capability to thwart all FDI attacks constructed with former branch susceptances only if (i) the number of branches l in the power system is not less than twice that of the system states n (i.e., l ⩾ 2n, where n + 1 is the number of buses); (ii) the susceptances of more than n branches, which cover all buses, are perturbed. Moreover, we prove that the state variable of a bus that is only connected by a single branch (no matter it is perturbed or not) can always be modified by the attacker. Nevertheless, in order to reduce the attack opportunities of potential attackers, we first exploit the impact of the susceptance perturbation magnitude on the dimension of the stealthy attack space, in which the attack vector is constructed with former branch susceptances. Then, we propose that, by perturbing an appropriate set of branches, we can minimize the dimension of the stealthy attack space and maximize the number of covered buses. Besides, we consider the increasing operation cost caused by the activation of MTD. Finally, we conduct extensive simulations to illustrate our findings with IEEE standard test power systems.

Metaverse is a vast virtual environment parallel to the physical world in which users enjoy a variety of services acting as an avatar. To build a secure living habitat, it’s vital to ensure the virtual-physical traceability that tracking a malicious player in the physical world via his avatars in virtual space. In this paper, we propose a two-factor authentication framework based on biometric-based authentication and chameleon signature. First, aiming at disguise in virtual space, we design an avatar’s two-factor identity model to ensure the verifiability of avatar’s virtual identity and physical identity. Second, facing at authentication efficiency and keys holding cost, we propose a chameleon collision signature algorithm to efficiently ensure that the avatar’s virtual identity is associated with its physical identity. Finally, aiming at impersonation in the physical world, we design two decentralized authentication protocols based on the avatar’s identity model and the chameleon collision signature to achieve real-time authentication on the avatar’s identity. Security analysis indicates that the proposed authentication framework guarantees the consistency and traceability of the avatar’s identity. Simulation experiments show that the framework not only completes the decentralized authentication between avatars but also achieves virtual-physical tracking.

With the rapid development of information and communications technology in DC microgrids (DCmGs), the deception attacks, which typically include false data injection and replay attacks, have been widely recognized as a significant threat. However, existing literature ignores the possibility of the intelligent attacker, who could launch deception attacks once obtaining necessary information by exploiting zero-day vulnerabilities or bribing insiders, to affect the system in an unforeseeable manner. In this paper, based on the observation that the primary control law of the power converter device in DCmGs is usually programmable, we propose a novel converter-based moving target defense (CMTD) strategy by proactively perturbing the primary control gains to defend against deception attacks. First, we study the impact of perturbing the primary control gains on the voltage stability in DCmGs and provide explicit conditions for the perturbation magnitude and frequency under which the voltage stability can be ensured. Then, we investigate the improved detectability against deception attacks under CMTD and present sufficient conditions under which these attacks can be detected. Finally, we conduct extensive MATLAB SIMULINK/PLECS based simulations and systematic hardware-in-the-loop based experiments to validate the effectiveness of CMTD.

Smart grid is confronted with cyberattacks due to the increasing dependence on cyberspace. False data injection attacks (FDIAs) represent a major type of cyberattacks that cannot be detected by the traditional bad data detection (BDD). The majority of existing researches focus on how to detect FDIAs, while little attention has been paid to obtaining the exact attack locations, which are crucial for deploying countermeasures in time. In this paper, we propose a neural network-based approach for online localizing FDIAs in AC power systems. The approach transforms the multi-dimensional time series measurements to their time-frequency and spectral graph representations, to jointly capture the temporal and spatial correlations in the spectral domain. The spectral decomposition could better characterize the latent properties of measurements, such as auto-correlations of a bus and key dependency between buses, which would be poorly represented in the time domain and vertex domain. Specifically, the proposed approach first adopts the short-time Fourier transform (STFT) and a two-channel convolutional neural network (2C-CNN) to model the spectral-temporal correlations. Then the spectral-spatial relationships are modeled with a graph convolutional network (GCN), which combines the nodal admittance matrix and physical property of power systems. The prior physical knowledge further improves our approach’s interpretability and localization performance. We evaluate the effectiveness of our proposed approach with comprehensive case studies on various standard test systems. Numerical results indicate the superiority of our approach over baselines.