用电力数据检测电动车充电器黑客攻击，准确率超99%。
A Kolmogorov-Arnold Network for Explainable Detection of Cyberattacks on EV Chargers
- 基于柯尔莫哥洛夫-阿诺德网络，仅靠用电量识别攻击
- 精度达99%，F1-score为92%，优于现有方法
- 能生成可解释的数学公式，适合安全监管场景
随着电动汽车（EV）普及和充电基础设施扩展，充电设备（EVSE）对通信的依赖使其面临网络攻击风险。本文提出一种基于柯尔莫哥洛夫-阿诺德网络（KAN）的新型框架，仅使用功率消耗数据检测电动车充电器的网络攻击。利用KAN建模高维非线性函数的能力及其固有的可解释性架构，该框架能有效区分正常与恶意充电行为。模型在包含超过10万次攻击案例的实验数据集上离线训练完成，训练后可部署于单个充电桩中实现实时异常行为检测。实验结果表明，所提KAN方法在检测电动车充电器攻击时，精度达到99%，F1-score为92%，显著优于现有检测手段。此外，该方法可提取表示检测决策的数学公式，解决了深度学习在网络安全中可解释性差的核心难题。本研究为构建安全且可解释的电动汽车充电基础设施迈出关键一步。
原文摘要 · Abstract (English)
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.
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