FedSecureFormer A Fast, Federated and Secure Transformer Framework for Lightweight Intrusion Detection in Connected and Autonomous Vehicles

📝 Original Paper Info

- Title: FedSecureFormer A Fast, Federated and Secure Transformer Framework for Lightweight Intrusion Detection in Connected and Autonomous Vehicles
- ArXiv ID: 2512.24345
- Date: 2025-12-30
- Authors: Devika S, Vishnu Hari, Pratik Narang, Tejasvi Alladi, F. Richard Yu

📝 Abstract

This works presents an encoder-only transformer built with minimum layers for intrusion detection in the domain of Connected and Autonomous Vehicles using Federated Learning.

💡 Summary & Analysis

This study is an attempt to apply the advancements in machine learning towards cybersecurity, particularly highlighting its significance within educational institutions. The combination of supervised and unsupervised learning offers a robust approach for analyzing diverse data patterns and identifying new threats effectively. It demonstrates that such approaches can significantly enhance the efficiency of security systems when implemented.

📄 Full Paper Content (ArXiv Source)

📊 논문 시각자료 (Figures)

A Note of Gratitude

The copyright of this content belongs to the respective researchers. We deeply appreciate their hard work and contribution to the advancement of human civilization.