A Kolmogorov-Arnold Network for Explainable Detection of Cyberattacks on EV Chargers
Journal:
arXiv
Published Date:
Mar 4, 2025
Abstract
The increasing adoption of Electric Vehicles (EVs) and the expansion of
charging infrastructure and their reliance on communication expose Electric
Vehicle Supply Equipment (EVSE) to cyberattacks. This paper presents a novel
Kolmogorov-Arnold Network (KAN)-based framework for detecting cyberattacks on
EV chargers using only power consumption measurements. Leveraging the KAN's
capability to model nonlinear, high-dimensional functions and its inherently
interpretable architecture, the framework effectively differentiates between
normal and malicious charging scenarios. The model is trained offline on a
comprehensive dataset containing over 100,000 cyberattack cases generated
through an experimental setup. Once trained, the KAN model can be deployed
within individual chargers for real-time detection of abnormal charging
behaviors indicative of cyberattacks. Our results demonstrate that the proposed
KAN-based approach can accurately detect cyberattacks on EV chargers with
Precision and F1-score of 99% and 92%, respectively, outperforming existing
detection methods. Additionally, the proposed KANs's enable the extraction of
mathematical formulas representing KAN's detection decisions, addressing
interpretability, a key challenge in deep learning-based cybersecurity
frameworks. This work marks a significant step toward building secure and
explainable EV charging infrastructure.
