An optimized deep learning model to ensure data integrity and security in IoT based e-commerce block chain application

Abstract

E-commerce, often known as electronic commerce, is the purchasing and selling of goods over the internet using electronic devices to share data. Banks and other financial institutions are frequently added as third-party platforms to traditional e-commerce platforms. As a result, it raises issues with integrity and cyber security. We suggest a deep learning-based strategy called the Hybrid Interactive Autodidactic School-Based Teaching-Learning Optimization (HIASTLO) algorithm to address these issues. The IoT-based e-commerce blockchain is used to extract and reject the various cyberattacks in the network, and deep learning is utilised to improve the weight and bias of the neural networks. We used a variety of performance indicators, including accuracy, precision, and recall, to identify cyberattacks. We also evaluated how well our work performed when compared to previous BSIoTNET, BCFC, DRNN, DNN-KNN, MOO-FS, LRNN, and HDLM efforts. Furthermore, MudraChain and NormaChain are used to examine the transaction time of our suggested task. The results show that our suggested work performs better than any other methods and offers highly secure internet services.