A Taxonomy of Cyber Defence Strategies Against False Data Attacks in
Smart Grid
- URL: http://arxiv.org/abs/2103.16085v1
- Date: Tue, 30 Mar 2021 05:36:09 GMT
- Title: A Taxonomy of Cyber Defence Strategies Against False Data Attacks in
Smart Grid
- Authors: Haftu Tasew Reda, Adnan Anwar, Abdun Naser Mahmood, and Zahir Tari
- Abstract summary: 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.
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.
However, it has also brought with it a large number of vulnerabilities resulting in breaches of data integrity, confidentiality and availability.
- Score: 3.88835600711547
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: 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 communications and the fast adoption of Internet of Things devices. 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 interest for both research and
industry. To this end, this paper presents a comprehensive review in the recent
advances of the defence countermeasures of the FDI attacks in the Smart Grid
infrastructure. Relevant existing literature are evaluated and compared in
terms of their theoretical and practical significance to the Smart Grid
cybersecurity. In conclusion, a range of technical limitations of existing
false data attack detection researches are identified, and a number of future
research directions are recommended.
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