Blockchain Networks for Cybersecurity Using Machine-Learning Algorithms

The application of blockchains along with machine-learning (ML) algorithms will provide safe data transactions because it is an unchanging kind of disseminated record that is suitable for loading information without depending on outside data. In addition, a few machine-learning algorithms using blockchains can prevent ransomware attacks. Even though a lot of work has gone into using blockchains with algorithms for network safety, too few exhaustive overviews have been conducted on blockchain frameworks that use such algorithms for online protection. A new advancement in the Internet of Things (IoT), in cutting-edge intelligent systems (ISs), can hold sufficient information to conduct big data analyses with high accuracy and low inertness in security. Thus, for conducting efficient and effective analytics on blockchain data, a neural network of deep learning (DL) could be a better option because it can identify all the layers. The research in this chapter focuses on cybersecurity in blockchains by using neural networks. The proposed method, which combines a multilayer perceptron (MLP) with long short-term memory (LSTM) models for online security to establish an arrangement that is based on market patterns, provides security to data transactions such as online payments.