Decentralized Blockchain-Integrated IoT Framework for Enhancing Cybersecurity and End-to-End Trust in Data Transmission

Abstract

The scale of IoT and IIoT systems developing is growing rapidly since they deploy in critical infrastructure which also created major security issues such as data breaches, unauthorized access, and centralized model lack of trust issues. It is search of this study to formulate a block-chain-based IoT network that provides secure, trustful and corrupted-proof transmission of data. What is new in the approach is the combination of the use of machine learning-based real-time anomaly detection and the Ethereum-based smart contracts in the creation of a decentralized and intelligent security layer. In the offered framework, models XGBoost are trained using the Edge-IIoTset dataset with maximum accuracy of detection of 98%. Compared to traditional centralized and standalone ML-based solutions, the hybrid system is found to be much more efficient in both initial detection (precision), trust enforcement, and resilience. The findings ratify that the framework can be deployed in sensitive IoT/IIoT networks based not only on its ability to identify the source of cyber threats and curb them in real-time without necessitating any update, but also on its potential to increase the transparency and traceability of the network.