【Author】
Otoum, Safa; Al Ridhawi, Ismaeel; Mouftah, Hussein
【Source】IEEE INTERNET OF THINGS JOURNAL
【Abstract】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 approximate to 0.93 and approximate to 0.96, respectively.
【Keywords】Adaptive solution; blockchain; critical infrastructures; federated learning (FL); reinforcement learning (RL); trustworthy
【标题】使用区块链支持的联邦学习保护关键的物联网基础设施
【摘要】网络可信度被认为是网络安全中非常关键的要素，它是通过积极的经验、保证、清晰和责任感而发展起来的。随着物联网 (IoT) 智能城市服务和应用程序的不断扩展，可信度变得更加引人注目。当今大多数网络可信赖的解决方案都被认为是不够的，特别是对于物联网设备可能暴露并容易受到威胁的关键应用程序。在本文中，我们提出了一个集成联邦学习和区块链的自适应框架，以实现网络的可信性和安全性。该解决方案能够将个人信任作为概率处理，并估计终端设备属于不同网络的信任值，但要达到安全标准。我们通过仿真评估和验证所提出的模型，以展示该框架在网络寿命、能耗和信任方面的有效性，并使用多个因素。结果表明，该模型保持了较高的准确率和检测率，分别接近 0.93 和 0.96。
【关键词】自适应解决方案；区块链；关键基础设施；联邦学习（FL）；强化学习（RL）；值得信赖
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