A Data-Driven Adaptive Cybersecurity Training Framework with Behavioral Validation

Abstract

Traditional cybersecurity awareness programs often fail to produce sustained behavioral change due to their static and non-personalized design. This paper presents CyberSense AI, a behavior-driven adaptive cybersecurity education framework that integrates a personalized learning engine, an interactive phishing simulation module, and a real-time threat intelligence system powered by a custom-trained machine learning (ML) model based on eXtreme Gradient Boosting (XGBoost). Beyond system implementation, we formally model the adaptive learning mechanism using a knowledge-state representation and reinforcement-inspired update rule to dynamically align question difficulty with user proficiency. To empirically validate the framework, we conducted a controlled pre-test/post-test study involving 60 participants randomly assigned to a control group and an experimental group. Results demonstrate a statistically significant improvement in phishing detection accuracy for the experimental group (p< 0.001, Cohen’s d =1.47), along with sustained two-week knowledge retention. Behavioral analytics further reveal a monotonic improvement curve across simulation sessions, a strong engagement–performance correlation (r =0.72), and progressive reduction in false-negative (FN) rates. A feature ablation and deployment latency analysis confirms that the XGBoost subsystem achieves sub-100ms response time, validating real-time mobile suitability. Collectively, these results establish CyberSense AI as a theoretically grounded and empirically validated framework for scalable, human-centered cybersecurity training.