BFL-SDWANTrust:在多控制器SD-WAN中实现安全的东西方通信的区块链联合学习支持的信任框架
Muddassar Mushtaq1, Kashif Kifayat2
1Department of Computer Science, Air University, Islamabad 44000, Pakistan.
Sensors (Basel, Switzerland)
|August 28, 2025
概括
本研究介绍了区块链联合学习支持的信任框架 (BFL-SDWANTrust),以提高软件定义广域网 (SD-WAN) 的安全性. 这种新的方法改善了恶意节点的检测,并确保了数据隐私,
科学领域:
- 计算机网络
- 网络安全
- 机器学习
背景情况:
- 软件定义广域网 (SD-WAN) 对企业网络可靠性至关重要,但容易受到恶意节点的攻击.
- 目前用于检测恶意节点的机器学习方法的准确性和隐私问题较低.
- 集中式SD-WAN架构引入了单点故障和性能瓶.
研究的目的:
- 提出一个新的区块链联合学习启用信任框架 (BFL-SDWANTrust),用于多控制器SD-WAN的安全东西通信.
- 通过增强恶意节点检测和确保数据隐私来解决传统SD-WAN的局限性.
- 消除对第三方或集中实体的网络运营和验证的依赖.
主要方法:
- 实施联合学习方法,在边缘节点实现本地模型培训,而无需中央数据聚合.
- 开发了一个基于区块链的网络来验证所有网络通信和恶意节点检测交易.
- 在InSDN数据集上对BFL-SDWANTrust模型进行了评估,并与基准恶意节点检测模型进行了比较.
主要成果:
- 与基准模型相比,BFL-SDWANTrust在各种指标上取得了更好的表现.
- 该模型显示了高准确度 (98.8%),精度 (98.0%),回忆 (97.0%) 和F1得分 (97.7%).
- 显著减少了训练 (12秒) 和测试 (3.1秒) 时间.
结论:
- 拟议的BFL-SDWANTrust有效地提高了多控制器SD-WAN中的安全性和隐私性.
- 联合学习和区块链集成为恶意节点检测提供了强大的分散解决方案.
- 该框架在异质SD-WAN环境中提供了改进的通用化,同时保持了网络实体的隐私.
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