# Fuse and Federate: Enhancing EV Charging Station Security with Multimodal Fusion and Federated Learning

arXiv: https://arxiv.org/abs/2506.06730 · HTML: https://mlsanity.com/paper/2506.06730/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 25 references that are in the map.
- First posted on arXiv 7 Jun 2025.

## Builds on (most cited first, 6 of 6)

- [Zero-X: A Blockchain-Enabled Open-Set Federated Learning Framework for Zero-Day Attack Detection in IoV](https://mlsanity.com/paper/2407.02969/) (2024)
- [Beyond Detection: Leveraging Large Language Models for Cyber Attack Prediction in IoT Networks](https://mlsanity.com/paper/2408.14045/) (2024)
- [Federated Learning for Zero-Day Attack Detection in 5G and Beyond V2X Networks](https://mlsanity.com/paper/2407.03070/) (2024)
- [Multi-agent Reinforcement Learning-based Network Intrusion Detection System](https://mlsanity.com/paper/2407.05766/) (2024)
- [A Life-long Learning Intrusion Detection System for 6G-Enabled IoV](https://mlsanity.com/paper/2407.15700/) (2024)
- [AI-Driven Fast and Early Detection of IoT Botnet Threats: A Comprehensive Network Traffic Analysis Approach](https://mlsanity.com/paper/2407.15688/) (2024)

## Abstract (arXiv)

The rapid global adoption of electric vehicles (EVs) has established electric vehicle supply equipment (EVSE) as a critical component of smart grid infrastructure. While essential for ensuring reliable energy delivery and accessibility, EVSE systems face significant cybersecurity challenges, including network reconnaissance, backdoor intrusions, and distributed denial-of-service (DDoS) attacks. These emerging threats, driven by the interconnected and autonomous nature of EVSE, require innovative and adaptive security mechanisms that go beyond traditional intrusion detection systems (IDS). Existing approaches, whether network-based or host-based, often fail to detect sophisticated and targeted attacks specifically crafted to exploit new vulnerabilities in EVSE infrastructure. This paper proposes a novel intrusion detection framework that leverages multimodal data sources, including network traffic and kernel events, to identify complex attack patterns. The framework employs a distributed learning approach, enabling collaborative intelligence across EVSE stations while preserving data privacy through federated learning. Experimental results demonstrate that the proposed framework outperforms existing solutions, achieving a detection rate above 98% and a precision rate exceeding 97% in decentralized environments. This solution addresses the evolving challenges of EVSE security, offering a scalable and privacypreserving response to advanced cyber threats