FL-IoV: comprehensive intrusion detection method for internet of vehicles based on federated learning

JIANG Yuchang; JIANG Libing; HUANG Kai

doi:10.11959/j.issn.2096-109x.2025047

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FL-IoV: comprehensive intrusion detection method for internet of vehicles based on federated learning

Topic: Cybersecurity for New Industrialization | 更新时间：2025-09-12

- FL-IoV: comprehensive intrusion detection method for internet of vehicles based on federated learning
- Chinese Journal of Network and Information Security Vol. 11, Issue 4, Pages: 127-140(2025)
- 作者机构：
  
  北京银联金卡科技有限公司深圳分公司，广东深圳 518049
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-109x.2025047
  CLC： TN915
- Received：08 February 2025，
  
  Revised：2025-05-08，
  
  Published：25 August 2025
- 稿件说明：
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蒋玉长, 蒋利兵, 黄凯. FL-IoV:基于联邦学习的综合车联网入侵检测方法[J]. 网络与信息安全学报, 2025, 11(4): 127-140.

Format: JIANG Y C, JIANG L B, HUANG K. FL-IoV: comprehensive intrusion detection method for internet of vehicles based on federated learning[J]. Chinese Journal of Network and Information Security, 2025, 11(4): 127-140.
蒋玉长, 蒋利兵, 黄凯. FL-IoV:基于联邦学习的综合车联网入侵检测方法[J]. 网络与信息安全学报, 2025, 11(4): 127-140. DOI： 10.11959/j.issn.2096-109x.2025047.

Format: JIANG Y C, JIANG L B, HUANG K. FL-IoV: comprehensive intrusion detection method for internet of vehicles based on federated learning[J]. Chinese Journal of Network and Information Security, 2025, 11(4): 127-140. DOI： 10.11959/j.issn.2096-109x.2025047.

摘要

现有车联网入侵检测系统面临计算开销过大及数据隐私限制两大挑战，难以高效部署与保障隐私。为此，提出一种基于联邦学习架构的车联网入侵检测方法FL-IoV，旨在于分布式边缘设备上协作完成检测任务，同时降低数据隐私泄露风险。通过将车载通信流量转化为图像并利用定制轻量级模型MobileNet-Tiny提取特征，FL-IoV在保证检测准确性的同时，可部署于资源受限设备。经在公开数据集Car-Hacking上验证，FL-IoV在本地环境和联邦环境下分别实现99.17%和98.51%的检测准确率，且在Raspberry Pi 4低算力设备上保持每个样本预测时间低于10 ms，表现出比现有方法更优的准确率和实时性。FL-IoV为车联网安全提供了一种既高效又保护隐私的入侵检测方案。

Abstract

Existing intrusion detection systems in the Internet of vehicles (IoV) were faced with two major challenges: excessive computational overhead and data privacy constraints

which had hindered efficient deployment and privacy protection. To address these issues

an IoV intrusion detection method based on a federated learning architecture

named FL-IoV

was proposed. It was designed to collaboratively accomplish detection tasks on distributed edge devices while the risk of data privacy breaches was reduced. Vehicular communication traffic within the IoV was converted into images

and a customized lightweight model

MobileNet-Tiny

was utilized to extract features

ensuring detection accuracy and enabling deployment on resource-constrained devices. When validated on the publicly available Car-Hacking dataset

FL-IoV achieved detection accuracies of 99.17% and 98.51% in local and federated IoV environments

respectively

and a per-sample prediction time of under 10 milliseconds was maintained on low-power devices such as the Raspberry Pi 4. These results demonstrate superior accuracy and real-time performance of FL-IoV compared to existing methods in IoV security. The successful application of FL-IoV provides an efficient and privacy-preserving new solution for IoV security.

关键词

Keywords

references

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