Inertia-weighted FedAvg plus RoCoF-augmented ChebyKAN controllers achieve 75% generalization on unseen IEEE-39 faults and beat centralized PFL on two of three stabilized cases at full decentralization.
A Kolmogorov-Arnold Network for Explainable Detection of Cyberattacks on EV Chargers
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
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.
years
2026 2representative citing papers
An off-the-shelf LLM prompted on tokenized Modbus traffic from public ICS datasets matches supervised baselines in normal-versus-critical classification accuracy while generating token-grounded audit records without any model updates.
citing papers explorer
-
Inertia-Informed Federated Learning Control Framework for Distributed Smart Grid Resilience
Inertia-weighted FedAvg plus RoCoF-augmented ChebyKAN controllers achieve 75% generalization on unseen IEEE-39 faults and beat centralized PFL on two of three stabilized cases at full decentralization.
-
Large Language Models as Explainable Cyberattack Detectors for Energy Industrial Control Systems
An off-the-shelf LLM prompted on tokenized Modbus traffic from public ICS datasets matches supervised baselines in normal-versus-critical classification accuracy while generating token-grounded audit records without any model updates.