학술논문

DFF-SC4N: A Deep Federated Defence Framework for Protecting Supply Chain 4.0 Networks
Document Type
Periodical
Source
IEEE Transactions on Industrial Informatics IEEE Trans. Ind. Inf. Industrial Informatics, IEEE Transactions on. 19(3):3300-3309 Mar, 2023
Subject
Power, Energy and Industry Applications
Signal Processing and Analysis
Computing and Processing
Communication, Networking and Broadcast Technologies
Security
Supply chains
Tools
Industries
Servers
Internet of Things
Training
Anomaly detection
federated learning
Internet of Things (IoT)
supply chain 4.0
Language
ISSN
1551-3203
1941-0050
Abstract
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.