Explainable Machine Learning Framework for Cybersecurity Awareness Prediction and Behavioral Pattern Mining Using Survey Data

This paper presents a machine learning framework for predicting cybersecurity awareness using survey-based behavioral data collected from university students and faculty members. Along with prediction, the framework applies SHAP-based explainable AI to understand the factors influencing the model’s decisions, association rule mining to identify relationships among cybersecurity behaviors, and clustering techniques to group users based on their security practices. The findings provide practical insights into user behavior and can help educational institutions develop more effective and targeted cybersecurity awareness programs.