Neuro-Fuzzy Modeling of Decision-Making in Cyber Defense Exercises Using ANFIS and Synthetic Data Augmentation

Decision-making in cyber defense exercises (CDX) is shaped by technical, emotional, motivational, and collaborative human factors under uncertainty and time pressure. This study proposes a human-centered Adaptive Neuro-Fuzzy Inference System (ANFIS) framework to model and predict Counterfactual Decision Reflection (CDR) outcomes in CDX environments. Two complementary datasets representing technical, emotional, motivational, and teamwork-related dimensions were collected from the international Lithuanian Armed Forces cyber defense exercise Amber Mist 2024 and analyzed using Spearman correlation, 3D regression surface modeling, fuzzy rule extraction, and ANFIS prediction to investigate the relationship between human factors and CDR. The results demonstrated that teamwork, communication, and collaboration have a stronger influence on decision stability than isolated technical competencies. Baseline ANFIS evaluation indicated that triangular membership functions provided the best generalization, while generalized bell functions achieved the lowest training errors. To improve model robustness, multiple synthetic data augmentation methods were evaluated. The augmented ANFIS models substantially improved predictive performance, reducing testing error values significantly. The findings confirm that synthetic-data-enhanced neuro-fuzzy modeling provides an effective and interpretable framework for analyzing human-centered cybersecurity decision-making processes in cyber defense exercises.