The rapid growth in Internet of Things (IoT) technology has become an integral part of today's industries forming the Industrial IoT (IIoT) initiative, where industries are leveraging IoT to improve communication and connectivity via emerging solutions like data analytics and cloud computing. Unfortunately, the rapid use of IoT has made it an attractive target for cybercriminals. Therefore, protecting these systems is of utmost importance. In this paper, we propose a federated transfer learning (FTL) approach to perform IIoT network intrusion detection. As part of the research, we also propose a combinational neural network as the centerpiece for performing FTL. The proposed technique splits IoT data between the client and server devices to generate corresponding models, and the weights of the client models are combined to update the server model. Results showcase high performance for the FTL setup between iterations on both the IIoT clients and the server. Additionally, the proposed FTL setup achieves better overall performance than contemporary machine learning algorithms at performing network intrusion detection.

翻译：物联网技术的快速发展已成为当今工业领域不可或缺的一部分，催生了工业物联网（IIoT）倡议。该倡议通过数据分析和云计算等新兴解决方案，旨在提升工业通信与连接能力。然而，物联网的广泛使用使其成为网络犯罪分子的重点攻击目标。因此，保护这些系统至关重要。本文提出一种联邦迁移学习（FTL）方法，用于实现工业物联网网络入侵检测。作为研究的一部分，我们还提出一种组合神经网络作为执行FTL的核心模型。该技术将物联网数据在客户端与服务器设备之间拆分，生成对应模型，并通过整合客户端模型权重来更新服务器模型。实验结果表明，在IIoT客户端与服务器之间的FTL迭代设置中，系统性能表现优异。此外，与当代机器学习算法相比，所提出的FTL框架在网络入侵检测任务中取得了更优的整体性能。