Artificial Intelligence-Based Smart Grid Vulnerabilities and Potential Solutions for Fake-Normal Attacks: A Short Review

TL;DR

This paper reviews AI-based approaches to identify vulnerabilities in smart grids, discusses challenges posed by advanced adversarial techniques like GANs, and suggests future research directions for enhancing cybersecurity in smart grid systems.

Contribution

It provides a concise overview of current AI security measures for smart grids, highlighting challenges and proposing future research avenues.

Findings

AI is increasingly used for smart grid cybersecurity.

GANs pose significant threats to existing AI defenses.

Future research should focus on robust AI methods against adversarial attacks.

Abstract

Smart grid systems are critical to the power industry, however their sophisticated architectural design and operations expose them to a number of cybersecurity threats, such as data tampering, data eavesdropping, and Denial of Service, among others. Artificial Intelligence (AI)-based technologies are becoming increasingly popular for detecting cyber assaults in a variety of computer settings, and several efforts have been made to secure various systems. The present AI systems are being exposed and vanquished because of the recent emergence of sophisticated adversarial systems such as Generative Adversarial Networks (GAN). The purpose of this short review is to outline some of the initiatives to protect smart grid systems, their obstacles, and what might be a potential future AI research direction