联邦学习下的电动汽车充电预测中的异常检测与缓解：在遭受网络攻击时
Federated Anomaly Detection and Mitigation for EV Charging Forecasting Under Cyberattacks
AI总结：
本文提出一种联邦学习框架，用于在遭受网络攻击时实现电动汽车充电预测中的异常检测与缓解，通过保护隐私、检测攻击并提升预测准确性。
摘要：
电动汽车（EV）充电基础设施面临日益严重的网络安全威胁，这些威胁可能严重破坏运营效率和电网稳定性。现有的预测技术受到缺乏结合鲁棒异常缓解方案和数据隐私保护的限制。因此，本文通过提出一个新颖的异常容忍联邦学习框架来解决这些挑战，该框架同时保护数据隐私、检测网络攻击并维持在对抗条件下可信的需求预测准确性。所提出的框架整合了三个关键创新：基于LSTM自编码器的分布式异常检测部署在每个联邦客户端、基于插值的异常数据缓解以保持时间连续性，以及联邦长短期记忆（LSTM）网络，使协作学习无需集中数据聚合。该框架在真实世界电动汽车充电基础设施数据集结合真实世界DDoS攻击数据集上进行了验证，提供了在现实威胁场景下的稳健验证。实验结果表明，联邦方法在集中式模型上实现了优越的性能，R2精度提高了15.2%，同时保持数据本地性。集成了网络攻击检测和缓解系统的产生可信数据集，提高了预测的可靠性，恢复了47.9%的攻击引起的性能下降，同时保持极高的精度（91.3%）和最小的误报率（1.21%）。所提出的架构增强了电动汽车基础设施规划、隐私保护的协作预测、网络安全韧性以及在分布式充电网络中快速恢复恶意威胁的能力。
英文摘要：
Electric Vehicle (EV) charging infrastructure faces escalating cybersecurity threats that can severely compromise operational efficiency and grid stability. Existing forecasting techniques are limited by the lack of combined robust anomaly mitigation solutions and data privacy preservation. Therefore, this paper addresses these challenges by proposing a novel anomaly-resilient federated learning framework that simultaneously preserves data privacy, detects cyber-attacks, and maintains trustworthy demand prediction accuracy under adversarial conditions. The proposed framework integrates three key innovations: LSTM autoencoder-based distributed anomaly detection deployed at each federated client, interpolation-based anomalous data mitigation to preserve temporal continuity, and federated Long Short-Term Memory (LSTM) networks that enable collaborative learning without centralized data aggregation. The framework is validated on real-world EV charging infrastructure datasets combined with real-world DDoS attack datasets, providing robust validation of the proposed approach under realistic threat scenarios. Experimental results demonstrate that the federated approach achieves superior performance compared to centralized models, with 15.2% improvement in R2 accuracy while maintaining data locality. The integrated cyber-attack detection and mitigation system produces trustworthy datasets that enhance prediction reliability, recovering 47.9% of attack-induced performance degradation while maintaining exceptional precision (91.3%) and minimal false positive rates (1.21%). The proposed architecture enables enhanced EV infrastructure planning, privacy-preserving collaborative forecasting, cybersecurity resilience, and rapid recovery from malicious threats across distributed charging networks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
