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Generative AI-based approach for player behavior analysis and gray area identification
1Center of Cyber Security Systems and Networks, Amrita Vishwa Vidyapeetham, Amritapuri, India.
Background:
Detecting exploitative or unethical player behavior in online gaming platforms is challenging due to ambiguous gray-area actions that are neither clearly legitimate nor illegal.
Methods:
This study presents an interpretable behavior analysis framework combining anomaly detection, synthetic data augmentation using Conditional Tabular GAN (CTGAN), and ensemble learning with post-hoc explainability. Datasets from a massively multiplayer online role-playing game (MMORPG) spanning 88 days (49,739 player sessions) were augmented to address class imbalance. Anomaly detection using an Encoder-Decoder GAN for Anomaly Detection (EGBAD) approach generated anomaly-aware features. A stacked ensemble model combining Random Forest, XGBoost, and Artificial Neural Networks was developed, with SHAP and LIME providing explanations for predictions.
Results:
The proposed framework achieved 95.98% accuracy, 0.915 ROC-AUC, and 0.90 macro F1-score, outperforming baseline models. The integration of CTGAN improved minority class recall by 5-7 percentage points, while EGBAD-derived anomaly features enhanced gray-area detection. Human-in-the-loop triage for low-confidence predictions (6.8% of cases) achieved 75% human-AI agreement with reduced false positives (21% decrease) and false negatives (17% decrease).
Discussion:
The framework successfully balances automated detection with human oversight, providing transparent, interpretable decisions for player behavior moderation while maintaining fairness and reducing wrongful enforcement actions.
