Aadish Jain,
Abhinav Kumar,
Gaurav Joshi,
Surya Pratap Singh,
Dheeraj Verma,
Praveen Kumar Agrawal,
- Student Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur Rajasthan India
- Student Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur Rajasthan India
- Student Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur Rajasthan India
- Student Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur Rajasthan India
- Research Scholar Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur Rajasthan India
- Associate Professor Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur Rajasthan India
Abstract
The integration of Information and Communication Technology (ICT) with traditional electric grids has led to the development of smart grids. However, this integration has also increased the risk of anomalies, such as cyber-attacks, metering fraud, electricity theft etc. False Data Injection Attacks are a class of cyber-attacks against power grid monitoring systems, where adversaries can inject false data to manipulate the grid’s operation. Metering frauds pertain to malicious customers com- promising their smart meters to report false readings to achieve financial gains illegally. As a result, there is a growing need for effective detection mechanisms to identify and mitigate anomalies in smart grids. One approach to detecting anomalies in smart grids is through the use of machine learning algorithms. These algorithms leverage the data generated by smart grid systems to identify anomalies that may indicate the presence of false data injection attacks. The detection of false data injection attacks in smart grids is a critical area of research, given the increasing risk of cyber-attacks in these systems. Machine learning algorithms, transfer learning, and deep learning-based methods have been proposed as effective mechanisms for detecting and mitigating anomalies in smart grids, ultimately contributing to the security and reliability of smart grid systems.
Keywords: Anomalies, Machine Learning, Information and Communication Technology, False Data Injection Attacks.
[This article belongs to Trends in Electrical Engineering(tee)]
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Trends in Electrical Engineering
Volume | 14 |
Issue | 02 |
Received | June 29, 2024 |
Accepted | July 22, 2024 |
Published | August 16, 2024 |