Protective Internet of Things (IoT) with Machine Learning and Blockchain Integration: Identifying Fraud and Predicting Abnormalities in Distributed Sensor Networks

Abstract

As the Internet of Things grows rapidly, more and more companies are using dispersed sensor networks. Companies in this sector focus on smart cities, intelligent transportation, healthcare, and industrial automation. The security and reliability of the Internet of Things are challenged by factors such as device heterogeneity, limited processing resources, and decentralised data generation, even as real-time data collecting and automation are taking place. There is a risk that data could be compromised due to threats. Systematic fraud or anomalous activity detection fails when it relies on centralised security. By incorporating AI and blockchain technology, this design enhances the reliability and security of distributed sensor networks. By analysing sensor-collected data, machine learning algorithms can detect fraudulent activity, unusual operational patterns, and real-time intrusions. Integrity of data, authentication of devices, and auditability of networks are all enhanced by blockchain technology, which generates an immutable distributed ledger. The immutability of ledger data makes this feasible. Securely enabling IoT nodes to work together without centralised authorities reduces the likelihood of failure points. This study demonstrates the use of decentralised trust systems and predictive intelligence to detect anomalies and secure data. Compared with conventional Internet of Things security measures, experimental results demonstrate higher detection accuracy, fewer false positives, and greater system resilience. To ensure the integrity of missioncritical data and the reliability of operations, the platform employs scalable, secure, and intelligent Iot.