Securing Critical IoT Infrastructures With Blockchain-Supported Federated Learning

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.

网络可信性被认为是网络安全中一个非常关键的要素，它通过积极的体验、保障、清晰性和责任来建立。随着物联网（IoT）智慧城市服务和应用的不断扩展，可信性变得更加重要。如今大多数网络可信性解决方案都被认为是不足的，特别是对于物联网设备可能暴露且容易受到攻击的关键应用。在本文中，我们提出了一个自适应框架，它集成了联邦学习和区块链，以实现网络可信性和安全性。该解决方案能够将个体的信任作为一种概率来处理，并在满足安全标准的情况下估计属于不同网络的终端设备的信任值。我们通过模拟对所提出的模型进行评估和验证，以展示该框架在网络寿命、能耗和使用多种因素的信任方面的有效性。结果表明，所提出的模型保持了较高的准确率和检测率，其值分别约为0.93和0.96。