# A Life-long Learning Intrusion Detection System for 6G-Enabled IoV

arXiv: https://arxiv.org/abs/2407.15700 · HTML: https://mlsanity.com/paper/2407.15700/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 16 references that are in the map.
- First posted on arXiv 22 Jul 2024.

## Builds on (most cited first, 7 of 7)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Experience Replay for Continual Learning](https://mlsanity.com/paper/1811.11682/) (2018)
- [5G-NIDD: A Comprehensive Network Intrusion Detection Dataset Generated over 5G Wireless Network](https://mlsanity.com/paper/2212.01298/) (2022)
- [Zero-X: A Blockchain-Enabled Open-Set Federated Learning Framework for Zero-Day Attack Detection in IoV](https://mlsanity.com/paper/2407.02969/) (2024)
- [Federated Learning for Zero-Day Attack Detection in 5G and Beyond V2X Networks](https://mlsanity.com/paper/2407.03070/) (2024)
- [Secure and Reliable Transfer Learning Framework for 6G-enabled Internet of Vehicles](https://mlsanity.com/paper/2111.05804/) (2021)
- [AntibotV: A Multilevel Behaviour-based Framework for Botnets Detection in Vehicular Networks](https://mlsanity.com/paper/2407.03506/) (2024)

## Built on by (most cited first, 4 of 4)

- [Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey](https://mlsanity.com/paper/2412.13840/) (2024)
- [BARTPredict: Empowering IoT Security with LLM-Driven Cyber Threat Prediction](https://mlsanity.com/paper/2501.01664/) (2025)
- [Zero-Day Botnet Attack Detection in IoV: A Modular Approach Using Isolation Forests and Particle Swarm Optimization](https://mlsanity.com/paper/2504.18814/) (2025)
- [Fuse and Federate: Enhancing EV Charging Station Security with Multimodal Fusion and Federated Learning](https://mlsanity.com/paper/2506.06730/) (2025)

## Abstract (arXiv)

The introduction of 6G technology into the Internet of Vehicles (IoV) promises to revolutionize connectivity with ultra-high data rates and seamless network coverage. However, this technological leap also brings significant challenges, particularly for the dynamic and diverse IoV landscape, which must meet the rigorous reliability and security requirements of 6G networks. Furthermore, integrating 6G will likely increase the IoV's susceptibility to a spectrum of emerging cyber threats. Therefore, it is crucial for security mechanisms to dynamically adapt and learn new attack patterns, keeping pace with the rapid evolution and diversification of these threats - a capability currently lacking in existing systems. This paper presents a novel intrusion detection system leveraging the paradigm of life-long (or continual) learning. Our methodology combines class-incremental learning with federated learning, an approach ideally suited to the distributed nature of the IoV. This strategy effectively harnesses the collective intelligence of Connected and Automated Vehicles (CAVs) and edge computing capabilities to train the detection system. To the best of our knowledge, this study is the first to synergize class-incremental learning with federated learning specifically for cyber attack detection. Through comprehensive experiments on a recent network traffic dataset, our system has exhibited a robust adaptability in learning new cyber attack patterns, while effectively retaining knowledge of previously encountered ones. Additionally, it has proven to maintain high accuracy and a low false positive rate.