Cy-Phy ADS: Cyber Physical Anomaly Detection Framework for EV Charging Systems

• Published in: IEEE transactions on transportation electrification p. 1
• Main Authors: Mavikumbure, Harindra S., Cobilean, Victor, Wickramasinghe, Chathurika S., Varghese, Benny J., Carlson, Barney, Rieger, Craig, Pennington, Timothy, Manic, Milos
• Format: Journal Article
• Language: English
• Published: United States IEEE 2024
• Subjects: Anomaly detection; Autoencoders; Behavioral sciences; deep neural network; Deep Neural Networks; Electric Vehicle Charging Systems; EV charging stations; Feature extraction; GENERAL AND MISCELLANEOUS; Health Monitoring; Monitoring; Resilience; Transportation; Unsupervised Learning; Visualization

Today's large-scale Electric Vehicle (EV) infrastructures are heavily dependent on information communication technologies to maintain their operation and to support communication within sub-system components as well as the outside world. These technologies are vulnerable to various cyber and ph...