Access control in smart contracts using digital identity management and machine learning to facilitate IoT exchanges

Access control in the blockchain network is one of the challenges we face with the growth of the blockchain network. In the blockchain network, the set of financial activities of users that require a digital signature is performed, this information is stored in the blockchain server. Manually signing digitally and verifying the authenticity of transactions is a time consuming and user-friendly process and is one of the reasons why blockchain technology is not fully accepted. In this paper, a new method is proposed based on a combination of clustering and classification methods. First, the data is labeled using the clustering method and then the labeled data is used to teach the SVM algorithm to determine healthy transactions. The proposed method is a machine learning method for access control that automatically blocks blockchain transactions and detects abnormal transactions. In order to evaluate the proposed method, atrium data have been tested and analyzed. And with the help of KMEANS clustering algorithm and machine vector support method, healthy transactions are detected from suspects, which shows the ability to identify with 89% accuracy