Enhancing cybersecurity in smart grids: Deep black box adversarial attacks and quantum voting ensemble models for blockchain privacy-preserving storage

Aurangzeb, Muhammad, Wang, Yifei, Iqbal, Sheeraz, Naveed, Ausnain, Ahmed, Zeeshan, Alenezi, Mohammed and Shouran, Mokhtar 2024. Enhancing cybersecurity in smart grids: Deep black box adversarial attacks and quantum voting ensemble models for blockchain privacy-preserving storage. Energy Reports 11 , 2493. 10.1016/j.egyr.2024.02.010

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Abstract

Smart grids are getting important in today’s power management, so with that, smart grid technologies are increasingly important too. There have been a lot of concerns about smart grid technologies being hacked, and as a result, some deep black box adversarial attacks have been conducted and presented. We propose a new experimental methodology for benchmarking smart grid security with black box attacks. Additionally, concerning the type of smart grids, Smart Power Grids, deep black box adversarial attacks which can be crafted using virtually no knowledge about the target due to the inherent complexity of content available in cryptographic libraries like SecLib or Bouncy Castle how it affects security of cyber-physical power systems. We identify potential impacts of deep black box attacks on Smart Power Grids as implemented by the Department of Energy in 1996, we evaluate existing protection methods, and we find out the pitfalls thereof. With the aim of overcoming the aforementioned drawbacks, we initiate a study on deep black box adversarial attacks against Smart Power Grids showing that statistically significant effects against a national Smart Power Grid are achievable with absolute security. We also probe detection of cyber security attacks on Smart Power Grids. We illustrate landscape of smart grids with numerous cyber threats and demonstrate the limitations of traditional security practices. We show the importance of machine learning to detect attacks and the unlikelihood of identification of dependable and efficient detection schemes. We describe quantum voting ensemble models as one of the most powerful techniques in the detection of cyber security attacks. Finally, we propose an experimental setup and evaluation criteria to detect cyber security attacks in smart grids using quantum voting ensemble models. Then, we talk about private data storage in blockchain based smart grid infrastructure. We give an introduction of block chain and its essentiality in smart grids. We discuss privacy issues in block chain based smart grids. We acknowledge the strength of privacy safeguards, but on the same wavelength, we realize their weaknesses. Next, we propose a quantum resistant encryption technique that enhances the privacy of smart grids. We propose quantum voting ensemble models as one of the most promising techniques to address the issue of private data storage in block chains. As a result, we provide a comparison between the proposed models and traditional approaches to privacy protection in smart grids based on an experimental performance review. Then, we propose a unified strategy to improve smart grid cyber security by incorporating deep black box attacks with quantum voting ensemble models. Finally, we disclose several benefits of such integration and perform an experimental evaluation to investigate the effectiveness of the unified approach. The results of our study identify security gaps in smart grids and propose state-of-the-art mechanisms to address them. The challenges of smart grids system require the amalgamation of blockchain, quantum voting ensemble models and deep black box adversarial attacks. We achieve this objective proposing a unified strategy. The results of this study will equally be helpful for future research and smart grid cyber security implementations.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Publisher:	Elsevier
ISSN:	2352-4847
Date of First Compliant Deposit:	5 March 2024
Date of Acceptance:	7 February 2024
Last Modified:	05 Mar 2024 14:00
URI:	https://orca.cardiff.ac.uk/id/eprint/166831

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