Uncovering how game bots get detected through Explainable Artificial Intelligence (XAI)

다국어 초록 (Multilingual Abstract)

Game bots are either bots using automated hardware or artificial intelligence bots using software for collecting assets in a game. Bots disturb other game players and destroy the environmental system of the games. For these reasons, the game industry has long had problems with game bots. The game industry put its best efforts into detecting the game bots using activity history in a learning-based detection method. These detection methods have captured game bots with high performance; however, they do not provide a reasonable explanation of the detection results. To solve this problem, in this paper, we investigate the explainabilities of game bot detection, utilizing a dataset from MMORG game AION, which includes both game logs from normal players and game bots. We conduct the detection of game bots through two classification models and analyze the detection process by applying explainable AI modules. We propose the verification of the explanation of the bot’s behavior, and the truthfulness has been evaluated. Besides, explainability contributes to minimizing false detection.

목차 (Table of Contents)

• 국문 요약 1
• Abstract 2
• 제 1장 Introduction 3
• 제 2장 Literature Review 7
• 2.1 Statistical Approaches 7
• 2.2 Machine Learning-based Approaches 7
• 2.3 Deep Learning-based Approaches 8
• 제 3장 Experiment 10
• 3.1 Dataset 10
• 3.2 Features 10
• 3.3 Experiment Environment 12
• 제 3장 Evaluation 15
• 4.1 Classification 15
• 4.2 Explanations 15
• 4.3 Evaluation for Explanations 20
• 4.4 Classification Improvements 21
• 제 3장 Discussion 23
• 제 3장 Conclusion 25
• Reference 27