Visible to the public Research on Enterprise Information Security Risk Assessment System Based on Bayesian Neural Network

TitleResearch on Enterprise Information Security Risk Assessment System Based on Bayesian Neural Network
Publication TypeConference Paper
Year of Publication2022
AuthorsDeng, Zijie, Feng, Guocong, Huang, Qingshui, Zou, Hong, Zhang, Jiafa
Conference Name2022 IEEE 2nd International Conference on Data Science and Computer Application (ICDSCA)
Date Publishedoct
KeywordsBayesian Regularization, Collaboration, composability, compositionality, computer theory, Data models, fuzzy theory, Human Behavior, Information security, Metrics, Neural networks, Organizations, policy governance, pubcrawl, resilience, Resiliency, risk assessment, security, simulation, Stability criteria, Training
AbstractInformation security construction is a social issue, and the most urgent task is to do an excellent job in information risk assessment. The bayesian neural network currently plays a vital role in enterprise information security risk assessment, which overcomes the subjective defects of traditional assessment results and operates efficiently. The risk quantification method based on fuzzy theory and Bayesian regularization BP neural network mainly uses fuzzy theory to process the original data and uses the processed data as the input value of the neural network, which can effectively reduce the ambiguity of language description. At the same time, special neural network training is carried out for the confusion that the neural network is easy to fall into the optimal local problem. Finally, the risk is verified and quantified through experimental simulation. This paper mainly discusses the problem of enterprise information security risk assessment based on a Bayesian neural network, hoping to provide strong technical support for enterprises and organizations to carry out risk rectification plans. Therefore, the above method provides a new information security risk assessment idea.
DOI10.1109/ICDSCA56264.2022.9988264
Citation Keydeng_research_2022