Blockchain Security Using Merkle Hash Zero Correlation Distinguisher for the IoT in Smart Cities
Software Engineering and Game Development
Internet of Things (IoT) data is one of the most important assets in business models for offering various ubiquitous and brilliant services. The IoT is provided with the advantage of susceptibility that cybercriminals and other malicious users. Even though smart cities are intended to extend productivity and efficiency, residents and authorities face risks when they avoid cybersecurity. The conventional blockchain methods were introduced to ensure the secure management and examination of the smart city big data. But, the blockchains are found to have computationally high costs, and failed to improve the security, not adequate resource-constrained IoT devices have been designated for smart cities. In order to address these issues, the proposed novel blockchain model called Blockchain Secured Merkle Hash Zero Correlation Distinguisher (BSMH-ZCD) is suitable for IoT devices within the cloud infrastructure. The objective of the BSMH-ZCD method is to enhance security and reduce the run time and computational overhead. Initially, the Merkle Hash tree is used to create the hash value with every transaction. Next, the Zero Correlation Distinguisher is applied to perform the data encryption and decryption operation for the ARX block for obtaining proficient secure data access in the IoT devices. Experimental assessment of the proposed BSMH-ZCD method and existing methods are carried out by using the taxi driver dataset and Novel Corona Virus2019 Dataset with different factors such as running time, computational complexity, and security with respect to a number of blocks and executions. By using the taxi driver dataset, the experimental results reveal that the BSMH-ZCD method performs better with a 19% improvement in security, 20% reduction of computational complexity, and 29% faster running time for IoT compared to existing works.
IEEE Internet of Things Journal
Digital Object Identifier (DOI)