DFF-SC4N: A Deep Federated Defence Framework for Protecting Supply Chain 4.0 Networks

• Published in: IEEE transactions on industrial informatics Vol. 19; no. 3; pp. 3300 - 3309
• Main Authors: Khan, Izhar Ahmed, Moustafa, Nour, Pi, Dechang, Hussain, Yasir, Khan, Nauman Ali
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-03-2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Anomaly detection; Communication networks; Cybersecurity; federated learning; Heterogeneity; Industries; Internet of Things; Internet of Things (IoT); Machine learning; Privacy; Security; Servers; supply chain 4.0; Supply chains; Training
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2021.3108811

The management of contemporary communication networks of supply chain (SC) 4.0 is becoming more complex due to the heterogeneity requirements of new devices concerning the integration of the Internet of Things in the legacy industry networks. Hence, it becomes a challenging task to secure networks of SC 4.0 from cyber-attacks and provide a robust and efficient defence framework that can resist sophisticated attacks. Machine learning-based intelligent detection algorithms are often trained at either a centralized or single server, which makes it difficult to train an effective model and also it violates privacy concerns if gathering data from other servers at the edge. Classical machine learning approaches function on the legacy group of data placed on a central or single server, which brands it the least favored choice for supply chain networks, with data privacy issues. To address these problems, this article proposes a federated learning-based efficient detection model named, DFF-SC4N, to proactively identify intrusions from SC 4.0 networks using distributed local data training. DFF-SC4N uses communication rounds in a federated learning manner having gated recurrent units by only sharing the learned parameters and keeps the data intact on local servers. The accuracy of the global model is optimized by an aggregating model, which updates from multiple servers and multiple SC 4.0 networks. Extensive experiments on real industrial network data demonstrate that the DFF-SC4N outperforms both centralized training models and state-of-the-art peer methods in protecting SC 4.0 networks.