A Taxonomy of Cyber Defence Strategies Against False Data Attacks in Smart Grids

Author:

Reda Haftu Tasew1ORCID,Anwar Adnan2ORCID,Mahmood Abdun Naser3ORCID,Tari Zahir4ORCID

Affiliation:

1. La Trobe University, Australia and Deakin University, Australia

2. Deakin University, Australia

3. La Trobe University, Australia

4. RMIT University, Australia

Abstract

The 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 communications (such as 5G/6G) and the fast adoption of Internet of Things devices (such as intelligent electronic devices and smart meters). While the synergy of a vast number of cyber-physical entities has allowed the Smart Grid to be much more effective and sustainable in meeting the growing global energy challenges, it has also brought with it a large number of vulnerabilities resulting in breaches of data integrity, confidentiality, and availability. False data injection (FDI) appears to be among the most critical cyberattacks and has been a focal point of interest for both research and industry. To this end, this article presents a comprehensive review of the recent advances in defence countermeasures of FDI attacks on the Smart Grid. Relevant existing works of literature are evaluated and compared in terms of their theoretical and practical significance to Smart Grid cybersecurity. In conclusion, a range of technical limitations of existing false data attack detection research is identified, and a number of future research directions are recommended.

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science,Theoretical Computer Science

Reference119 articles.

1. False data injection attacks against state estimation in electric power grids;Liu Yao;ACM Transactions on Information and System Security (TISSEC),2011

2. Adnan Anwar. Nov. 2017. Data-Driven Stealthy Injection Attacks on Smart Grid. Ph.D. Dissertation. School of Engineering and Information Technology, University of New South Wales.

3. Data-driven approach for state prediction and detection of false data injection attacks in smart grid;Reda Haftu Tasew;Journal of Modern Power Systems and Clean Energy,2023

4. Y. Gu, T. Liu, D. Wang, X. Guan, and Z. Xu. 2013. Bad data detection method for smart grids based on distributed state estimation. In 2013 IEEE International Conference on Communications (ICC). IEEE, Budapest, Hungary, 4483–4487.

5. M. Göl and A. Abur. 2015. A modified Chi-Squares test for improved bad data detection. In 2015 IEEE Eindhoven PowerTech. IEEE, Eindhoven, Netherlands, 1–5.

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