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Generative AI-based approach for player behavior analysis and gray area identification.

Vinay K¹, Sriram Sankaran¹

¹Center of Cyber Security Systems and Networks, Amrita Vishwa Vidyapeetham, Amritapuri, India.

Frontiers in Artificial Intelligence

|April 6, 2026

Summary

This summary is machine-generated.

This study introduces an interpretable framework for detecting unethical player behavior in online games. It combines anomaly detection and ensemble learning, improving accuracy and fairness in moderation.

Keywords:

CTGAN)GANs LIME)bot detection explainable AI (SHAP generative AI (VAEs gray-area behaviors human-in-the-loop moderation online gaming ecosystems player behavior analysis

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Area of Science:

* Computational Social Science
* Artificial Intelligence in Gaming
* Cybersecurity and Online Behavior Analysis

Background:

* Detecting nuanced, unethical player behavior in online games presents significant challenges due to the prevalence of ambiguous actions.
* Existing methods struggle to differentiate between legitimate and illicit activities, leading to potential unfairness in moderation.

Purpose of the Study:

* To develop an interpretable behavior analysis framework for accurately detecting unethical player conduct in online gaming.
* To enhance the detection of gray-area behaviors through advanced machine learning techniques.
* To integrate human oversight for improved accuracy and fairness in moderation decisions.

Main Methods:

* Implementation of a framework combining anomaly detection (Encoder-Decoder GAN for Anomaly Detection - EGBAD) and ensemble learning (Random Forest, XGBoost, Artificial Neural Networks).
* Synthetic data augmentation using Conditional Tabular GAN (CTGAN) to address class imbalance in player behavior datasets.
* Application of post-hoc explainability methods (SHAP, LIME) for transparent decision-making.

Main Results:

* The framework achieved high performance metrics: 95.98% accuracy, 0.915 ROC-AUC, and 0.90 macro F1-score, surpassing baseline models.
* Conditional Tabular GAN (CTGAN) integration boosted minority class recall by 5-7%, while EGBAD features improved gray-area detection.
* Human-in-the-loop triage demonstrated 75% human-AI agreement, reducing false positives by 21% and false negatives by 17%.

Conclusions:

* The proposed framework effectively balances automated detection with human oversight for transparent and fair player behavior moderation.
* Interpretability ensures that moderation decisions are understandable, reducing wrongful enforcement actions.
* The study demonstrates a significant advancement in addressing complex behavioral analysis in online gaming environments.