Mushtaq Muddassar, Kifayat Kashif
Department of Computer Science, Air University, Islamabad 44000, Pakistan.
College of Computing and Intelligent Systems, University of Khorfakkan, Sharjah 18119, United Arab Emirates.
Sensors (Basel). 2025 Aug 21;25(16):5188. doi: 10.3390/s25165188.
Software-Defined Wide-Area Networks (SD-WAN) efficiently manage and route traffic across multiple WAN connections, enhancing the reliability of modern enterprise networks. However, the performance of SD-WANs is largely affected due to malicious activities of unauthorized and faulty nodes. To solve these issues, many machine-learning-based malicious-node-detection techniques have been proposed. However, these techniques are vulnerable to various issues such as low classification accuracy and privacy leakage of network entities. Furthermore, most operations of traditional SD-WANs are dependent on a third-party or a centralized party, which leads to issues such single point of failure, large computational overheads, and performance bottlenecks. To solve the aforementioned issues, we propose a Blockchain Federated-Learning-Enabled Trust Framework for Secure East-West Communication in Multi-Controller SD-WANs (BFL-SDWANTrust). The proposed model ensures local model learning at the edge nodes while utilizing the capabilities of federated learning. In the proposed model, we ensure distributed training without requiring central data aggregation, which preserves the privacy of network entities while simultaneously improving generalization across heterogeneous SD-WAN environments. We also propose a blockchain-based network that validates all network communication and malicious node-detection transactions without the involvement of any third party. We evaluate the performance of our proposed BFL-SDWANTrust on the InSDN dataset and compare its performance with various benchmark malicious-node-detection models. The simulation results show that BFL-SDWANTrust outperforms all benchmark models across various metrics and achieves the highest accuracy (98.8%), precision (98.0%), recall (97.0%), and F1-score (97.7%). Furthermore, our proposed model has the shortest training and testing times of 12 s and 3.1 s, respectively.
软件定义广域网（SD-WAN）能够高效管理并在多个广域网连接上路由流量，提升现代企业网络的可靠性。然而，由于未经授权和存在故障的节点的恶意活动，SD-WAN的性能受到很大影响。为了解决这些问题，已经提出了许多基于机器学习的恶意节点检测技术。然而，这些技术容易受到各种问题的影响，如分类准确率低和网络实体的隐私泄露。此外，传统SD-WAN的大多数操作依赖于第三方或集中方，这导致了诸如单点故障、大量计算开销和性能瓶颈等问题。为了解决上述问题，我们提出了一种用于多控制器SD-WAN中安全东西向通信的基于区块链联邦学习的信任框架（BFL-SDWANTrust）。所提出的模型在利用联邦学习能力的同时，确保边缘节点进行本地模型学习。在所提出的模型中，我们确保分布式训练而无需中央数据聚合，这在保护网络实体隐私的同时，还能提高跨异构SD-WAN环境的泛化能力。我们还提出了一种基于区块链的网络，该网络在不涉及任何第三方的情况下验证所有网络通信和恶意节点检测交易。我们在InSDN数据集上评估了所提出的BFL-SDWANTrust的性能，并将其性能与各种基准恶意节点检测模型进行了比较。仿真结果表明，BFL-SDWANTrust在各种指标上均优于所有基准模型，并实现了最高的准确率（98.8%）、精确率（98.0%）、召回率（97.0%）和F1分数（97.7%）。此外，我们提出的模型的训练和测试时间分别最短，为12秒和3.1秒。
