Advanced Bad Data Injection Attack and Its Migration in Cyber-Physical Systems

Deng, Wenping; Yang, Ziyu; Xun, Peng; Zhu, Peidong; Wang, Baosheng

doi:10.3390/electronics8090941

Cited by 6 publications

(3 citation statements)

References 31 publications

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“…d) False data injection attack defense: It is difficult to defend against FDIAs due to their concealment [131]. Two FDIA detection methods are proposed in [132]: 1) stateestimation based detection, and 2) machine-learning based detection.…”

Section: Malware Attackmentioning

confidence: 99%

A Survey on Cyber–Physical Systems Security

Gao

Cong

et al. 2023

IEEE Internet Things J.

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Section: Malware Attackmentioning

confidence: 99%

A Survey on Cyber–Physical Systems Security

Gao

Cong

et al. 2023

IEEE Internet Things J.

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“…Attack studies will also aid the development of defenses against attacks or ways to lessen their effects. Algorithms for attack-resistant control and bad data detection are examples of countermeasures [28].…”

Section: Cps Layers and Attack Modelingmentioning

confidence: 99%

A Hybrid Physical Co-Simulation Smart Grid Testbed for Testing and Impact Analysis of Cyber-Attacks on Power Systems: Framework and Attack Scenarios

Abdelrahman,

Kharchouf,

Nguyen

et al. 2023

Energies

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With the deployment of numerous innovative smart grid technologies in modern power systems, more real-time communication and control are required due to the complexity and proliferation of grid-connected systems, making a power system a typical cyber-physical system (CPS). However, these systems are also exposed to new cyber vulnerabilities. Therefore, understanding the intricate interplay between the cyber and physical domains and the potential effects on the power system of successful attacks is essential. For cybersecurity experimentation and impact analysis, developing a comprehensive testbed is needed. This paper presents a state-of-the-art Hybrid Physical Co-simulation SG testbed at FIU developed for in-depth studies on the impact of communication system latency and failures, physical events, and cyber-attacks on the grid. The Hybrid SGTB is designed to take full advantage of the benefits of both co-simulation-based and physical-based testbeds. Based on this testbed, various attack strategies are tested, including man-in-the-middle (MitM), denial-of-service (DoS), data manipulation (DM), and setting tampering (change) on various power system topologies to analyze their impacts on grid stability, power flow, and protection reliability. Our research, which is based on extensive testing on several testbeds, shows that using hybrid testbeds is justified as both practical and effective.

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“…Then the control center loses the normal control ability of the physical equipment, which causes the power system to collapse, even leads to a large-scale power failure. In recent years, more kinds of FDIA have emerged, the attacker can use network vulnerabilities to tamper with the measurement data, control data, even the equipment information with the minimum cost, which cause the large-scale chain failures [8]. However, the defense aiming at FDIA become more difficult because of the great concealment of FDIA.…”

Section: Introductionmentioning

confidence: 99%

A Novel False Data Injection Attack Detection Model of the Cyber-Physical Power System

Cao

Wang

et al. 2020

IEEE Access

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The False data injection attack (FDIA) against the Cyber-Physical Power System (CPPS) is a kind of data integrity attack. With more and more cyber vulnerabilities detected out, different types of FDIAs are emerging as severe threats to the stable operation of CPPS gradually. In this paper, the invasion pathway of the FDIA against CPPS is explored in detail, and a novel FDIA detection model based on ensemble learning is further provided. First, a pseudo-sample database is built to assist the training and evaluation of this model, and it's more important to update the model in the future. Furthermore, the optimal feature set is extracted to characterize the behavior of the FDIA, which improves the precision of the FDIA detection model. Finally, a focal-loss-lightgbm (FLGB) ensemble classifier is constructed to detect the FDIA behavior automatically and accurately. We illustrated the performance of this model by a fusion of measurement data and power system audit logs. This model utilizes the offline training way, the conclusion shows the high precision and stability of this model, which ensures the stable operation of the smart grid and improves the FDIA resistance ability of the CPPS. INDEX TERMS CPPS, FDIA detection model, invasion pathway analysis, ensemble learning.

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Advanced Bad Data Injection Attack and Its Migration in Cyber-Physical Systems

Cited by 6 publications

References 31 publications

A Survey on Cyber–Physical Systems Security

A Survey on Cyber–Physical Systems Security

A Hybrid Physical Co-Simulation Smart Grid Testbed for Testing and Impact Analysis of Cyber-Attacks on Power Systems: Framework and Attack Scenarios

A Novel False Data Injection Attack Detection Model of the Cyber-Physical Power System

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