Information Security Education Journal Bingjie Su 12 2 2025 https://doi.org/10.6025/isej/2025/12/2/57-64 https://www.dline.info/isej/fulltext/v12n2/isejv12n2_3.pdf 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.