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| Title | Decentralised Iot Authentication Using Blockchain And Machine Learning: The Trust Circle Framework |
|---|---|
| ID_Doc | 17620 |
| Authors | Jagdish B.V.; Induraj A.; Vikas M.N.; Akshay M.; Naik C. |
| Year | 2025 |
| Published | Proceedings of 8th International Conference on Inventive Computation Technologies, ICICT 2025 |
| DOI | http://dx.doi.org/10.1109/ICICT64420.2025.11005150 |
| Abstract | The Internet of Things (IoT) is projected to connect more than 50 billion devices by 2020, and its rapid growth has significantly influenced sectors such as smart cities, healthcare, agriculture, and military operations. IoT supports applications like smart appliances, waste management, and traffic monitoring, contributing to reductions in CO2 emissions. At the core of IoT functionality lies the Wireless Sensor Network (WSN), which consists of distributed sensors that collect and transmit data for various uses. The primary aim of IoT is to enable seamless connectivity between devices, allowing any object to access information in real time. However, due to the characteristics of wireless communication, device limitations, and system range, IoT systems face serious security threats such as identity spoofing and message tampering. Traditional centralized security approaches, like Public Key Infrastructure (PKI), are often costly and ineffective for large-scale IoT networks, leading to challenges in integration and scalability. To enhance security, reliability, and scalability in IoT, this research proposes the Trust Circle Framework, a decentralized authentication system that incorporates blockchain technology and machine learning. The framework emphasizes adaptability to the decentralized nature of IoT without reliance on centralized security systems. It enables the addition of new services, facilitates smooth device communication, and aligns with multiple IoT standards. Experimental outcomes show that this approach offers improved authentication accuracy, decreased latency, and heightened resistance to cyber threats compared to conventional methods. Blockchain technology provides immutable identity verification and protects against data manipulation and spoofing. Moreover, machine learning algorithms enable adaptive threat detection, allowing the system to distinguish between legitimate and harmful nodes. This innovative, trust-based strategy addresses the limitations of existing security measures and delivers a scalable and secure solution for IoT ecosystems, fostering robust networks capable of meeting the demands of modern interconnected environments. © 2025 IEEE. |
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