A Framework for Mining Collectively-Behaving Bots in MMORPGs

TL;DR

This paper introduces BotTRep, a framework that uses trajectory representation learning and clustering to detect collectively-behaving bots in MMORPGs, aiding game moderation efforts.

Contribution

The paper presents a novel, unsupervised framework combining trajectory representation learning and clustering to identify bot behaviors in MMORPGs.

Findings

Effective clustering of bot trajectories demonstrated

Facilitates identification of collective bot behaviors

Supports game moderation with visualized patterns

Abstract

In MMORPGs (Massively Multiplayer Online Role-Playing Games), abnormal players (bots) using unauthorized automated programs to carry out pre-defined behaviors systematically and repeatedly are commonly observed. Bots usually engage in these activities to gain in-game money, which they eventually trade for real money outside the game. Such abusive activities negatively impact the in-game experiences of legitimate users since bots monopolize specific hunting areas and obtain valuable items. Thus, detecting abnormal players is a significant task for game companies. Motivated by the fact that bots tend to behave collectively with similar in-game trajectories due to the auto-programs, we developed BotTRep, a framework that comprises trajectory representation learning followed by clustering using a completely unlabeled in-game trajectory dataset. Our model aims to learn representations for in-game trajectory sequences so that players with contextually similar trajectories have closer embeddings. Then, by applying DBSCAN to these representations and visualizing the corresponding moving patterns, our framework ultimately assists game masters in identifying and banning bots.