Securing Critical IoT Infrastructures With Blockchain-Supported Federated Learning

• Published in: IEEE internet of things journal Vol. 9; no. 4; pp. 2592 - 2601
• Main Authors: Otoum, Safa, Ridhawi, Ismaeel Al, Mouftah, Hussein
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 15.02.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation models; Adaptive solution; Blockchain; Blockchains; Collaboration; Computational modeling; critical infrastructures; Cryptography; Data models; Energy consumption; Federated learning; federated learning (FL); Internet of Things; Model accuracy; Network security; reinforcement learning (RL); Security; Training; Trust; Trustworthiness; trustworthy
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2021.3088056

Network trustworthiness is considered a very crucial element in network security and is developed through positive experiences, guarantees, clarity, and responsibility. Trustworthiness becomes even more compelling with the ever-expanding set of Internet of Things (IoT) smart city services and applications. Most of today's network trustworthy solutions are considered inadequate, notably for critical applications where IoT devices may be exposed and easily compromised. In this article, we propose an adaptive framework that integrates both federated learning and blockchain to achieve both network trustworthiness and security. The solution is capable of dealing with individuals' trust as a probability and estimates the end devices' trust values belonging to different networks subject to achieving security criteria. We evaluate and verify the proposed model through simulation to showcase the effectiveness of the framework in terms of network lifetime, energy consumption, and trust using multiple factors. Results show that the proposed model maintains high accuracy and detection rates with values of <inline-formula> <tex-math notation="LaTeX">\approx 0.93 </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">\approx 0.96 </tex-math></inline-formula>, respectively.