A Study of the System Failures in the Network Security Using Information Processing
Bingjie Su
Information Security Education Journal , Vol. 12, No. 2, 2025.
DOI: https://doi.org/10.6025/isej/2025/12/2/57-64
Link: https://www.dline.info/isej/fulltext/v12n2/isejv12n2_3.pdf
Abstract:
The paper investigates common cause failures (CCFs) in secure computer operating systems using data
mining techniques. As secure systems underpin critical sectors like government, defense, and healthcare,
understanding and mitigating complex, interrelated failures stemming from hardware, software, or human
errors is essential. The study proposes a novel Common Cause Failure Score to quantify and compare security
performance across systems. Using association rule mining and decision tree algorithms (e.g., C5.0), the
research uncovers hidden relationships among failure events. The Naive Bayes classifier is also employed to
manage multidimensional data and categorize risk levels. Experiments involve preprocessing real world
system failure data, applying statistical and machine learning models, and validating results using tools
such as ROC curves and confusion matrices. Health Index (HI) modeling and life prediction techniques further
assess system reliability over time. Findings demonstrate that integrating multiple data mining approaches
enhances the accuracy of failure prediction, root cause analysis, and risk mitigation strategies. The study
concludes that these methods significantly improve system stability, support informed decision making by
administrators, and provide a robust framework for evaluating and selecting high performance, secure
computing systems in complex network environments.