# Show Me Your Account: Detecting MMORPG Game Bot Leveraging Financial Analysis with LSTM

## TL;DR

This paper introduces a novel MMORPG bot detection method leveraging financial analysis and LSTM to identify behavioral patterns, providing a sustainable solution against evolving bot strategies.

## Contribution

The paper proposes a new bot detection approach using financial data analysis with LSTM, addressing limitations of previous behavioral and social feature-based methods.

## Key findings

- Achieved effective detection performance with actual game data.
- LSTM efficiently recognizes time-series financial data patterns.
- Proposed method offers a sustainable solution for evolving bots.

## Abstract

With the rapid growth of MMORPG market, game bot detection has become an essential task for maintaining stable in-game ecosystem. To classify bots from normal users, detection methods are proposed in both game client and server-side. Among various classification methods, data mining method in server-side captured unique characteristics of bots efficiently. For features used in data mining, behavioral and social actions of character are analyzed with numerous algorithms. However, bot developers can evade the previous detection methods by changing bot's activities continuously. Eventually, overall maintenance cost increases because the selected features need to be updated along with the change of bot's behavior. To overcome this limitation, we propose improved bot detection method with financial analysis. As bot's activity absolutely necessitates the change of financial status, analyzing financial fluctuation effectively captures bots as a key feature. We trained and tested model with actual data of Aion, a leading MMORPG in Asia. Leveraging that LSTM efficiently recognizes time-series movement of data, we achieved meaningful detection performance. Further on this model, we expect sustainable bot detection system in the near future.