Federated Anomaly Detection and Mitigation for EV Charging Forecasting Under Cyberattacks

2025-11-22

AI Summary

To address the dual challenges of degraded load forecasting accuracy and data privacy leakage in electric vehicle (EV) charging infrastructure under cyberattacks, this paper proposes a synergistic framework integrating anomaly detection, attack mitigation, and federated learning. The method innovatively combines a distributed LSTM autoencoder for anomaly detection, interpolation-driven anomaly correction, and a privacy-preserving federated LSTM network—enabling decentralized collaborative modeling without sharing raw data. Evaluated on real-world DDoS attack data, the framework achieves a 15.2% improvement in R² score, restores 47.9% of post-attack forecasting performance, attains 91.3% anomaly detection accuracy, and maintains a low false positive rate of 1.21%. These results demonstrate substantial gains in forecasting robustness and system resilience against adversarial cyber threats.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Computer Vision: Adversarial Attacks & Robustness
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

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.

Problem

• Detecting cyberattacks on EV charging infrastructure while preserving data privacy
• Mitigating anomalies in charging forecasts without centralized data aggregation
• Maintaining accurate demand prediction under adversarial conditions using federated learning

Innovation

• Federated learning framework for anomaly detection
• LSTM autoencoder-based distributed anomaly detection
• Interpolation-based mitigation for temporal continuity