BP Neural Network Algorithm for Computer Network Security Evaluation

Abstract

BP neural network algorithm has been used in the evaluation of computer network security. This algorithm is also called back propagation learning. It is a supervised learning technology, which can be used to identify the most important nodes in a given neural network, and then use this information to determine how these nodes should change their behavior. The application of back propagation (BP) neural network algorithm helps us understand the working principle behind this principle. The main goal of this algorithm is to detect any malicious activities in the computer network. Its working principle is learning and pattern recognition. It is very fast and efficient, and does not require much time for training or testing. It can be easily implemented in any type of system, such as embedded system, desktop computer, etc. It has been proved that it can be applied to identify malware with high accuracy without much impact on performance. This paper analyzes the existing information system risk assessment methods in the process of evaluating complex information systems. Due to the high complexity and strong interdependence of information systems, it is difficult to carry out risk assessment work. A computer network security assessment study based on BP neural network algorithm is proposed to improve the traditional analytic hierarchy process risk assessment method, which can complete the dynamic assessment process of network security risks, It can greatly improve the accuracy of evaluation and meet the actual needs of network security; Applying the improved analytic hierarchy process to the evaluation process of computer network information systems in Tibet can better identify hidden risk factors in computer network information systems, and comprehensively defend and improve network operation indicators.