Treffer: ChainGuard 6G+: A Secure and Private Architecture for Wireless Communication Using Federated Learning and Blockchain in IoT Networks.

The advent of 6G wireless communication systems and the widespread proliferation of Internet of Things devices have necessitated advanced frameworks for secure, private, and intelligent data management. ChainGuard 6G+, a novel privacy-preserving architecture, which integrates Federated Learning with Blockchain, is introduced in this paper to offer data security, integrity, and anomaly detection features for IoTenabled 6G networks. FL facilitates decentralized model training across distributed edge nodes, thus keeping local data on-device with model updates shared. This ensures user privacy, particularly valuable in sensitive applications such as healthcare, financial services, and industrial IoT networks. For further strengthening privacy, Differential Privacy is applied by introducing statistical noise into model updates, masking individual contributions without degrading learning accuracy. Blockchain is incorporated as an immutable ledger to record model parameters and training securely, enabling traceability and tamper-evident model provenance. Rolebased access control for secure data and model access, end-to-end encryption, and secure transmission protocols are included in the architecture. Experimental results demonstrate the efficacy of the system under consideration using a 6G Network Slice Security Attack Detection Dataset, with synthetic and real attacks on various network slices. Performance evaluation reveals that ChainGuard 6G+ not only ensures data privacy but also has excellent detection rates against DoS, DDoS, and spoofing attacks. The proposed framework achieves an overall attack detection accuracy of 99.1%, implemented and experimented using Python, revealing its promise as a secure, scalable solution for future wireless secure communication networks. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Intelligent Systems & Internet of Things is the property of American Scientific Publishing Group and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)