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Smart city article details
| Title | Empowering Energy Transition: Detecting Cyber Threats In Ev Charging Infrastructure Through Ml And Xai Analysis |
|---|---|
| ID_Doc | 22912 |
| Authors | Patel T.; Jhaveri R.H.; Sidana S.; Benedetto F. |
| Year | 2024 |
| Published | Proceedings of the 2024 International Conference on Sustainable Energy: Energy Transition and Net-Zero Climate Future, ICUE 2024 |
| DOI | http://dx.doi.org/10.1109/ICUE63019.2024.10795550 |
| Abstract | With the adoption and usage levels on a newer scale in modern society, there is also equal effort or attention given to technological improvements for electric vehicle infrastructure and the EV Charging Network. With electric vehicles becoming more and more pervasive in number, there has been an even greater need to secure the EV charging networks and ensure they are safe from problems such as energy theft from the networks. However, most of these traditional security protocols cannot adapt to the ever-evolving landscape of cyber-attacks against electric vehicle chargers. With the outlined challenges, this research work aims to propose a Machine Learning-based approach in conjunction with the use of Explainable Artificial Intelligence to be capable of detecting various kinds of cyber-attacks, including DoS and advanced firmware modifications. The XAI brings together and ensures clarity in the models. XAI provides explanations about the details behind the much-needed opacity in how machine-learning models work and provides the understanding and confidence required by stakeholders and regulators that security will be deployed. It works to heighten cybersecurity not only in EV charging networks but also toward the general vision of creating resilient smart cities. It will help in developing confidence in and belief about the fast-evolving world of electric vehicle infrastructure. © 2024 IEEE. |
| Author Keywords | Cyber Security; Electric Vehicles; Machine Learning; Renewable Energy; Smart City |
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