Enhancing Cybersecurity through GAN-Augmented and Hybrid Feature Selection Machine Learning Models: A Case Study on EVSE Data

Year 2024, , 61 - 70, 29.06.2024

Hayriye Tanyıldız , Canan Batur Şahin , Özlem Batur Dinler

https://doi.org/10.46572/naturengs.1495489

Abstract

Electric Vehicle Charging Stations (EVCSs) are critical components of modern transportation infrastructure. However, smart grid integrations create new security vulnerabilities. Cyber attacks can damage EVCSs, cause financial losses, and compromise user security. Traditional security measures are not enough. By analyzing the large data sets produced by EVCSs, machine learning (ML) can detect anomalies and provide predictive maintenance. The increasing importance of EVCSs in smart grid infrastructure necessitates taking advanced security measures. Machine learning-based intrusion detection systems represent a promising solution to the dynamic and complex cyber threats facing these critical infrastructures. Through continuous learning and adaptation, ML can provide a robust defense mechanism that ensures the security and reliability of EVCSs in the face of evolving cyber threats.

Keywords

Electric Vehicle Charging Stations, GAN, Anaomaly Detection, Cyber Attack

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