Machine Learning Based Analysis of Cyber-Attacks Targeting Smart Grid Infrastructure

The transformation of traditional electric power systems into smart grids has allowed for improved monitoring and control capabilities. However, this evolution has also brought about new challenges in the form of malware and vulnerabilities, creating significant security concerns. To address this issue, sensors are being deployed to the grid, leading to a large influx of data that needs to be analyzed for accurate results. Machine learning algorithms are being utilized to process this data and extract important information. This paper explores the application of various machine learning algorithms in smart grids, with a specific focus on their use in cybersecurity. We identify different types of cyber threats affecting smart grids and examine defense strategies to counter these threats. Through this study, we provide insights into the potential of machine learning in addressing the cybersecurity challenges in smart grids.