In the dynamic field of the Industrial Internet of Things (IIoT), the networks are increasingly vulnerable to a diverse range of cyberattacks. This vulnerability necessitates the development of advanced intrusion detection systems (IDSs). Addressing this need, our research contributes to the existing cybersecurity literature by introducing an optimized Intrusion Detection System based on Deep Transfer Learning (DTL), specifically tailored for heterogeneous IIoT networks. Our framework employs a tri-layer architectural approach that synergistically integrates Convolutional Neural Networks (CNNs), Genetic Algorithms (GA), and bootstrap aggregation ensemble techniques. The methodology is executed in three critical stages: First, we convert a state-of-the-art cybersecurity dataset, Edge_IIoTset, into image data, thereby facilitating CNN-based analytics. Second, GA is utilized to fine-tune the hyperparameters of each base learning model, enhancing the model’s adaptability and performance. Finally, the outputs of the top-performing models are amalgamated using ensemble techniques, bolstering the robustness of the IDS. Through rigorous evaluation protocols, our framework demonstrated exceptional performance, reliably achieving a 100% attack detection accuracy rate. This result establishes our framework as highly effective against 14 distinct types of cyberattacks. The findings bear significant implications for the ongoing development of secure, efficient, and adaptive IDS solutions in the complex landscape of IIoT networks.

在工业物联网 (IIoT) 的动态领域，网络越来越容易受到各种网络攻击。此漏洞需要开发先进的入侵检测系统（IDS）。为了满足这一需求，我们的研究通过引入基于深度迁移学习 (DTL) 的优化入侵检测系统（专为异构 IIoT 网络量身定制），为现有的网络安全文献做出了贡献。我们的框架采用三层架构方法，协同集成卷积神经网络（CNN）、遗传算法（GA）和引导聚合集成技术。该方法分三个关键阶段执行：首先，我们将最先进的网络安全数据集 Edge_IIoTset 转换为图像数据，从而促进基于 CNN 的分析。其次，利用遗传算法对每个基础学习模型的超参数进行微调，增强模型的适应性和性能。最后，使用集成技术合并表现最好的模型的输出，增强了 IDS 的鲁棒性。通过严格的评估协议，我们的框架展示了卓越的性能，可靠地实现了 100% 的攻击检测准确率。这一结果表明我们的框架对于 14 种不同类型的网络攻击非常有效。这些发现对于在复杂的 IIoT 网络环境中持续开发安全、高效和自适应的 IDS 解决方案具有重要意义。