Imminent Threat with Authentication Methods for AI Data Using Blockchain Security

Abstract

Since the announcement of Satoshi Nakamoto’s Bitcoin policy document in 2008, blockchain has become one of the most widely discussed techniques for implementing safety storage and processing through decentralized, approved, peer-to-peer networks. This study described peer-reviewed literature, which uses cryptocurrency for cybersecurity purposes and offers a comprehensive overview of the most commonly used application areas of blockchains. This main forward-looking study further illuminates the possible directions for science, education, and practice on blockchain and cyberprotection, such as IoT blockchain security, as well as the need for data analysis of blockchain safe data. Analyses of this data increase the value of the latest machine learning (ML) technologies. There is a logical quantity of data required by ML for correct decisions. Data reliability and sharing in ML are very critical for improving the accuracy of performance. The combination of these two technologies will yield extremely accurate results (ML and BT). In this paper, we present a detailed review of ML adoption to make mobile platforms based on BT more resilient against attacks. Examples of such support systems as support vector machines (SVM) and bagging and deep learning (DL) algorithms can be used to evaluate attacks on a blockchain network, including convolutional neural network (CNN) and long short-term memory (LSTM). Actually, various traditional ML techniques are available. Furthermore, we include the use of both technologies in a variety of smart applications, including UAV, Smart Grid (SG), healthcare, and smart towns and cities. Future technological issues and concerns are also debated. Finally, we discuss the study model with a thesis.