Integrating ML and Blockchain for Consumer-Centric Approaches in Securing Data Transmission on IoT Networks

Data security and privacy are becoming more essential due to the quick expansion of “Internet of Things (IoT)”, particularly in consumer-focused applications. To improve the security of data transmission in IoT networks, this study provides a novel architecture that combines Blockchain with Machine Learning (ML) to address critical security threats like data integrity breaches, unauthorised access and cyber-attacks. The suggested solution uses a hybrid blockchain paradigm that combines public and private blockchain capabilities to ensure data integrity and safe access management. Machine learning algorithms, including anomaly detection and classification techniques, are utilised to quickly monitor and analyse network traffic, identify possible risks, and enhance the system’s resilience against cyber-attacks. The platform also uses neural networks based on transfer learning for dynamic trust validation and IP rotation using moving target defense (MTD) mechanisms to reduce attacks and strengthen the security. Also, optimised resource management techniques are used to improve stability and reduce computational complexities. Combining these technologies strengthens data security while giving users more control over their data. This method offers a workable solution for the safe implementation of IoT networks in consumer applications by addressing the issues of scalability, latency, and energy efficiency.