Affiliation:
1. Manipal University Jaipur, India
Abstract
With the adoption of internet of things (IoT) devices, the security challenges associated with their implementation are a focus of research and development. This study examines the integration of cutting-edge technologies, machine learning knowledge, and blockchain to strengthen the payment security of IoT systems. The research explores the vulnerabilities present in traditional IoT architectures and presents a new approach that exploits machine learning algorithms embedded in a blockchain box. The core of this methodology involves implementing machine learning models to analyze large data sets generated by IoT devices, identifying anomalous patterns indicating possible security risks. However, locks are stored and validated by a secure lock, which ensures an immutable and transparent record of system activities. Blockchain integration alone improves data integrity, although it also establishes a decentralized consent mechanism, which reduces the risk of single points of failure and unauthorized access. This study evaluates the proposed solution through empirical analysis, considering real-world IoT scenarios. A comparative assessment was conducted against conventional security measures, demonstrating the effectiveness of machine learning-based blockchain approaches in detecting and mitigating diverse security threats. Additionally, this research addresses scalability and performance considerations, providing insight into the practical implementation of the proposed solution in the dynamic and resource-constrained environments typical of IoT ecosystems. In conclusion, this study contributes to the ongoing discourse on strengthening the security of IoT systems by merging machine learning and blockchain technologies. The proposed framework not only addresses challenges in today's security, but also emphasis on the foundation of infrastructure which is strong and adaptive also proficient of addressing the evolving IoT threat landscape in the near future.