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

arXiv: https://arxiv.org/abs/2202.07050 · HTML: https://mlsanity.com/paper/2202.07050/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 29 references that are in the map.
- First posted on arXiv 14 Feb 2022.

## Builds on (most cited first, 3 of 3)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Cyber-Security in Smart Grid: Survey and Challenges](https://mlsanity.com/paper/1809.02609/) (2018)
- [Regression-based Online Anomaly Detection for Smart Grid Data](https://mlsanity.com/paper/1606.05781/) (2016)

## Abstract (arXiv)

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