Article: Prediction of computer network security situation based on machine learning Journal: International Journal of Intelligent Information and Database Systems (IJIIDS) 2026 Vol.18 No.7 pp.1 - 18 Abstract: In order to address security issues such as malicious intrusion or attacks in the current network, this paper uses machine learning technology to predict the security development trend of computer networks to ensure the normal operation of computers. Firstly, this paper collects data related to network security and performs feature processing on the collected data to achieve data purification. Secondly, this paper chooses radial basis function neural network (RBFNN) to train the processed data and uses guided learning algorithm to optimise the machine model. Then, this paper evaluates the model through evaluation indicators such as accuracy and recall rate, and finally establishes a system or service that can receive and predict network security events in real time. Research has found that the error value of using RRFNN for network security situation prediction is less than 0.03, which improves the prediction accuracy by 0.02 compared to using support vector machine systems. Inderscience Publishers - linking academia, business and industry through research

Title: Prediction of computer network security situation based on machine learning

Authors: Ye Liu

Addresses: Department of Information Engineering, Fushun Vocational Technology Institute (Fushun Teachers College), Fushun, 113122, Liaoning, China

Abstract: In order to address security issues such as malicious intrusion or attacks in the current network, this paper uses machine learning technology to predict the security development trend of computer networks to ensure the normal operation of computers. Firstly, this paper collects data related to network security and performs feature processing on the collected data to achieve data purification. Secondly, this paper chooses radial basis function neural network (RBFNN) to train the processed data and uses guided learning algorithm to optimise the machine model. Then, this paper evaluates the model through evaluation indicators such as accuracy and recall rate, and finally establishes a system or service that can receive and predict network security events in real time. Research has found that the error value of using RRFNN for network security situation prediction is less than 0.03, which improves the prediction accuracy by 0.02 compared to using support vector machine systems.

Keywords: intrusion detection system; IDS; data cleaning; radial basis function neural network; RBFNN; mean-square error; MSE.

DOI: 10.1504/IJIIDS.2026.155882

International Journal of Intelligent Information and Database Systems, 2026 Vol.18 No.7, pp.1 - 18

Received: 14 Mar 2025
Accepted: 03 Sep 2025
Published online: 25 Aug 2026 *

Title: Prediction of computer network security situation based on machine learning

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