Title: Machine learning and deep learning techniques for detecting and mitigating cyber threats in IoT-enabled smart grids: a comprehensive review

Authors: Aschalew Tirulo; Siddhartha Chauhan; Kamlesh Dutta

Addresses: Department of Computer Science and Engineering, NIT Hamirpur, HP 177005, India ' Department of Computer Science and Engineering, NIT Hamirpur, HP 177005, India ' Department of Computer Science and Engineering, NIT Hamirpur, HP 177005, India

Abstract: The confluence of the internet of things (IoT) with smart grids has ushered in a paradigm shift in energy management, promising unparalleled efficiency, economic robustness and unwavering reliability. However, this integrative evolution has concurrently amplified the grid's susceptibility to cyber intrusions, casting shadows on its foundational security and structural integrity. Machine learning (ML) and deep learning (DL) emerge as beacons in this landscape, offering robust methodologies to navigate the intricate cybersecurity labyrinth of IoT-infused smart grids. While ML excels at sifting through voluminous data to identify and classify looming threats, DL delves deeper, crafting sophisticated models equipped to counteract avant-garde cyber offensives. Both of these techniques are united in their objective of leveraging intricate data patterns to provide real-time, actionable security intelligence. Yet, despite the revolutionary potential of ML and DL, the battle against the ceaselessly morphing cyber threat landscape is relentless. The pursuit of an impervious smart grid continues to be a collective odyssey. In this review, we embark on a scholarly exploration of ML and DL's indispensable contributions to enhancing cybersecurity in IoT-centric smart grids. We meticulously dissect predominant cyber threats, critically assess extant security paradigms, and spotlight research frontiers yearning for deeper inquiry and innovation.

Keywords: smart grid; cyber threats; cybersecurity; internet of things; IoT; deep learning; machine learning.

DOI: 10.1504/IJICS.2024.141601

International Journal of Information and Computer Security, 2024 Vol.24 No.3/4, pp.284 - 321

Received: 06 Aug 2023
Accepted: 11 Oct 2023
Published online: 26 Sep 2024 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article