相关实验视频
BHFVAL:区块链启用的等级联合变量自动编码器框架,用于安全检测车辆网络中的入侵.
G Elavel Visuvanathan1,2, Md Shohel Sayeed3, Sumendra Yogarayan4
1Centre for Intelligent Cloud, CoE of Advanced Cloud, Faculty of Information Science and Technology, Multimedia University, Jalan Ayer Keroh Lama, Bukit Beruang, 75450, Melaka, Malaysia.
Scientific reports
|December 1, 2025
概括
本研究介绍了用于汽车互联网 (IoV) 的安全学习模型,使用区块链启用层次联合变量自编码器学习 (BHFVAL) 来增强数据安全性和在具有挑战性的网络条件下分散学习效率.
科学领域:
- 车辆网络 车辆网络
- 分散式的学习学习
- 网络安全 网络安全
背景情况:
- 车辆系统中的集中架构是脆弱的.
- 现有的联合学习在敌对环境中是低效的.
- 有限的计算资源和不同的网络条件对 IoV 中的安全数据处理构成了挑战.
研究的目的:
- 为汽车互联网 (IoV) 引入一个智能,有效和安全的学习模型.
- 解决集中式架构的脆弱性和在敌对环境中联合学习的低效性.
- 在有限的计算资源和不同的网络条件下增强分散式学习的有效性.
主要方法:
- 区块链启用的等级联合变异自编码器学习 (BHFVAL) 模型,具有基于声誉的拜占庭式故障容忍 (RBFT) 机制.
- 轻量级边缘计算 (LEC) 模块用于基于近距离的处理,以最大限度地减少通信延迟.
- Osprey优化算法 (OOA) 用于超参数优化和基于圆曲线的同型加密 (ECHE) 的轻量级安全通信协议 (LSCP) 用于安全的V2X通信.
主要成果:
- UNSW-NB15数据集实现了96.83%的准确性和96.65%的F1分数 (IID),以及95.74%的准确性和95.40%的F1分数 (非IID).
- CIC-IDS-2017数据集实现了97.36%的准确性,97.2%的AUROC和97.1%的F1分数 (IID),以及96.40%的准确性和96.20%的F1分数 (非IID).
- 在分散的车辆网络中表现出强大的检测性能和适应性.
结论:
- BHFVAL框架可靠,适应性强,并且对分散的,对隐私敏感的车辆网络有效.
- 该模型成功地提高了在具有挑战性的 IoV 环境中安全性和学习效率.
- 提出的方法显著提高了车辆系统中的数据处理安全性和学习效率.
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