相关论文: Cyberattack Detection in Large-Scale Smart Grids u…

This paper presents a Temporal Graph Neural Network (TGNN) framework for detection and localization of false data injection and ramp attacks on the system state in smart grids. Capturing the topological information of the system through the…

The increasing digitization of smart grids has improved operational efficiency but also introduced new cybersecurity vulnerabilities, such as False Data Injection Attacks (FDIAs) targeting Automatic Generation Control (AGC) systems. While…

Smart power grid enables intelligent automation at all levels of power system operation, from electricity generation at power plants to power usage at households. The key enabling factor of an efficient smart grid is its built-in…

The smart grid concept has transformed the traditional power grid into a massive cyber-physical system that depends on advanced two-way communication infrastructure to integrate a myriad of different smart devices. While the introduction of…

As one of the most sophisticated attacks against power grids, coordinated cyber-physical attacks (CCPAs) damage the power grid's physical infrastructure and use a simultaneous cyber attack to mask its effect. This work proposes a novel…

This paper develops a novel graph convolutional network (GCN) framework for fault location in power distribution networks. The proposed approach integrates multiple measurements at different buses while taking system topology into account.…

Motivated by the recent cyber attack on the Ukrainian power grid, we study cyber attacks on power grids that affect both the physical infrastructure and the data at the control center. In particular, we assume that an adversary attacks an…

False Data Injection Attacks (FDIAs) pose a significant threat to smart grid infrastructures, particularly Home Area Networks (HANs), where real-time monitoring and control are highly adopted. Owing to the comparatively less stringent…

Quick and accurate detection of cyber attack is key to the normal operation of the smart grid system. In this paper, a joint state estimation and sequential attack detection method for a given bus with grid frequency drift is proposed that…

Deep neural networks (DNNs) are becoming commonplace in critical applications, making their susceptibility to backdoor (trojan) attacks a significant problem. In this paper, we introduce a novel backdoor attack detection pipeline, detecting…

In this study, we introduce a novel unsupervised countermeasure for smart grid power systems, based on generative adversarial networks (GANs). Given the pivotal role of smart grid systems (SGSs) in urban life, their security is of…

In this paper, we consider the problems of state estimation and false data injection detection in smart grid when the measurements are corrupted by colored Gaussian noise. By modeling the noise with the autoregressive process, we estimate…

As an important cyber-physical system (CPS), smart grid is highly vulnerable to cyber attacks. Amongst various types of attacks, false data injection attack (FDIA) proves to be one of the top-priority cyber-related issues and has received…

Graph convolutional networks (GCNs) are currently the most promising paradigm for dealing with graph-structure data, while recent studies have also shown that GCNs are vulnerable to adversarial attacks. Thus developing GCN models that are…

Most traditional false data injection attack (FDIA) detection approaches rely on a key assumption, i.e., the power system can be accurately modeled. However, the transmission line parameters are dynamic and cannot be accurately known during…

Data analysis and monitoring on smart grids are jeopardized by attacks on cyber-physical systems. False data injection attack (FDIA) is one of the classes of those attacks that target the smart measurement devices by injecting malicious…

Modern electric power grid, known as the Smart Grid, has fast transformed the isolated and centrally controlled power system to a fast and massively connected cyber-physical system that benefits from the revolutions happening in the…

Deep Neural Networks have proven to be highly accurate at a variety of tasks in recent years. The benefits of Deep Neural Networks have also been embraced in power grids to detect False Data Injection Attacks (FDIA) while conducting…

As power systems evolve with increased integration of renewable energy sources, they become more complex and vulnerable to both cyber and physical threats. This study validates a centralized Dynamic State Estimation (DSE) algorithm designed…

The national highway traffic safety administration (NHTSA) identified cybersecurity of the automobile systems are more critical than the security of other information systems. Researchers already demonstrated remote attacks on critical…