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Cyber Resilience and Privacy Preservation in Smart Grid using Machine Learning Techniques

Project Incharge : Dr. Manjula G. Nair

Cyber Resilience and Privacy Preservation in Smart Grid using Machine Learning Techniques

Cyber Security Threats are emerging as one of the major concerns in a smart grid mainly because of the extensive use of heterogeneous communication technologies. As the fields of Detection and Prediction of these attacks have been explored in momentum making a Smart Grid more Resilient to these attacks is still challenging. Cyber Resilience which can be briefly defined as the ability of the grid to recover from a cyber-attack, is of utmost concern since it can be considered the self-defense mechanism of a smart grid. Machine Learning can be implemented in Cyber Resilience Techniques to make it more efficient and find optimal strategies to help the system recover. The Machine Learning Algorithm learns from historical data of the system. Owing to the restrictions imposed by the security and privacy policies of the utility systems, the availability of historical data poses one of the greatest challenges in the implementation of such techniques. So the model has to be developed with significant consideration of privacy preservation and data security of the systems. Therefore, the proposed project aims at designing a Cyber Resilient system using Machine Learning techniques on forth with a Privacy Preservation algorithm that assures the data security of the system. This research has a collaboration with University of Trento, Italy under student mobility scheme for bi-lateral agreement, on the development of a digital twin for smart grid and another collaboration for a project with West Virginia University, USA.

Research Progress

The first phase of the research is based on the detection of cyber attacks in a smart grid environment using machine learning algorithms. Implementation of an Autoencoder- based False Data Injection Attacks detection in smart grids has been successful, with a detection rate of 98%. The algorithm has also been validated through a comparative analysis to prove the superiority of the proposed method.

Autoencoder Architecture
Flowchart of the Proposed Algorithm

Collaborations with Universities / Industry Partnerships

Dr. Sarika Khushalani Solanki

Associate Professor

Lane Department of Computer Science and Electrical Engineering

West Virginia University

Title: Development of a Digital Twin of Smart Grid Cyber-Physical System

Dr. Fabrizio Granelli

Full Professor, University of Trento Dept. of Information Engineering and Computer Science

Via Sommarive 9 38123, T, Italy

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