Blockchain transaction model based on malicious node detection network

In this day and age, blockchain technology has become very popular. More and more transactions have been completed through the blockchain platform. The blockchain trading platform is fast, low-cost and high security. Many companies use blockchain for online transactions. However, with the increase in transaction volume and transaction scale, malicious users (nodes) appear, and malicious nodes participate in the blockchain network to carry out improper transactions, which brings huge losses to the transaction party. This paper proposes a Blockchain transaction model based on a malicious node detection network to ensure the safety of transaction users and enable the blockchain transaction to be traded in a safe environment. Aiming at the problem of malicious nodes deliberately submitting malicious information or obtaining Bitcoin through malicious behaviors on the blockchain, a malicious node detection model (MNDM) based on a hierarchical neural network is proposed. The hierarchical network model can calculate the key attributes according to the behavior of the nodes to detect abnormal nodes and kick them out of the blockchain system. The proposed model can avoid unnecessary losses caused by malicious nodes participating in data transmission and transactions and stop losses in time. The constructed model is called a hierarchical network model because it has two significant levels and realizes the reduction of parameter volume and the calculation of key information on the levels. Comparative tests are given in this paper. The validity of the model is proved by calculating the accuracy, precision, recall rate, and F1 score of the malicious node detection model.