Related papers: False Data Injection Attack on Electric Vehicle-As…
Utility companies are increasingly leveraging residential demand flexibility and the proliferation of smart/IoT devices to enhance the effectiveness of residential demand response (DR) programs through automated device scheduling. However,…
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…
Small-Signal Stability (SSS) is crucial for the control of power grids. However, False Data Injection (FDI) attacks against SSS can impact the grid stability, hence, the security of SSS needs to be studied. This paper proposes a formal…
False data injection attacks (FDIA) are a main category of cyber-attacks threatening the security of power systems. Contrary to the detection of these attacks, less attention has been paid to identifying the attacked units of the grid. To…
False Data Injection (FDI) attacks are a significant threat to modern power systems. Although numerous research studies have focused on FDI attacks on power systems, these studies have primarily concentrated on designing or detecting DC FDI…
The evolution of the traditional power system towards the modern smart grid has posed many new cybersecurity challenges to this critical infrastructure. One of the most dangerous cybersecurity threats is the False Data Injection (FDI)…
Training and evaluating false data injection attack (FDIA) detectors for power systems is constrained by data scarcity. Operational grid measurements are commercially sensitive, and hand-crafted attacks fail to capture complex…
False Data Injection (FDI) attacks are one of the challenges that the modern power system, as a cyber-physical system, is encountering. Designing AC FDI attacks that accurately address the physics of the power systems could jeopardize the…
In this paper a new class of cyber attacks against state estimation in the electric power grid is considered. This class of attacks is named false data injection attacks. We show that with the knowledge of the system configuration an…
The surging usage of electric vehicles (EVs) demand the robust deployment of trustworthy electric vehicle charging station (EVCS) with millisecond range latency and massive machine to machine communications where 5G could act. However, 5G…
Modern advances in sensor, computing, and communication technologies enable various smart grid applications. The heavy dependence on communication technology has highlighted the vulnerability of the electricity grid to false data injection…
False data injection attacks (FDIAs) pose a significant security threat to power system state estimation. To detect such attacks, recent studies have proposed machine learning (ML) techniques, particularly deep neural networks (DNNs).…
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…
Intelligently designed false data injection (FDI) attacks have been shown to be able to bypass the $\chi^2$-test based bad data detector (BDD), resulting in physical consequences (such as line overloads) in the power system. In this paper,…
The urgent need for the decarbonization of power girds has accelerated the integration of renewable energy. Concurrently the increasing distributed energy resources (DER) and advanced metering infrastructures (AMI) have transformed the…
This paper studies the vulnerability of large-scale power systems to false data injection (FDI) attacks through their physical consequences. Prior work has shown that an attacker-defender bi-level linear program (ADBLP) can be used to…
State estimation is of considerable significance for the power system operation and control. However, well-designed false data injection attacks can utilize blind spots in conventional residual-based bad data detection methods to manipulate…
recent literature has proposed various detection and identification methods for FDIAs, but few studies have focused on a solution that would prevent such attacks from occurring. However, great strides have been made using deep learning to…
Modern urban railways extensively use computerized sensing and control technologies to achieve safe, reliable, and well-timed operations. However, the use of these technologies may provide a convenient leverage to cyber-attackers who have…
Accurate and reliable dynamic state quantities of generators are very important for real-time monitoring and control of the power system. The emergence of cyber attacks has brought new challenges to the state estimation of generators.…