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All Journal Bulletin of Electrical Engineering and Informatics
Trinadh Naidu, Kamparapu V V Satya
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Electrical & Electronics Engineering

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Securing electric vehicle charging stations from adversarial cyber attacks using hybrid detection models Jaladanki, Ravindra Babu; Kolluru, Pavan Kumar; Shaik, Nagul; Trinadh Naidu, Kamparapu V V Satya; Veeraiah, Duggineni; Pradhan, Anita; N. P. Sairam, Rallabandi Ch. S.
Bulletin of Electrical Engineering and Informatics Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i3.11144

Abstract

Electric vehicle charging infrastructure (EVCI) has become essential. However, these infrastructures are increasingly vulnerable to cyber threats, particularly through spoofing and adversarial attacks on charging ports. This paper introduces a robust anomaly detection framework leveraging long short-term memory (LSTM) based autoencoders to identify anomalies in electric vehicle (EV) charging port current magnitudes. A simulated EVCI setup is developed in MATLAB/Simulink to capture charging behaviors under normal and adversarial scenarios. To generate adversarial data, the fast gradient sign method (FGSM) is employed. The reconstructed outputs from the LSTM-autoencoder (LSTM-AE) are statistically compared to real-time observations using the Kolmogorov–Smirnov (KS) test to detect anomalies. The framework achieves a high detection accuracy of 98.5%, demonstrating strong resilience against cyber-injected data anomalies and setting a foundation for enhanced EVCI cybersecurity.
Co-Authors Jaladanki, Ravindra Babu Kolluru, Pavan Kumar N. P. Sairam, Rallabandi Ch. S. Pradhan, Anita Shaik, Nagul Veeraiah, Duggineni