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

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 serverside. 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 timeseries movement of data, we achieved meaningful detection performance. Further on this model, we expect sustainable bot detection system in the near future.

Effective Bots’ Detection for Online Smartphone Game Using Multilayer Perceptron Neural Networks

“…ey behave in a programmed way and repetitively do patterned actions to collect game assets. Bots can gather game wealth faster than normal players because the bot program does not get tired [1,2]. Consequently, bots will endanger the rights of mobile online game players.…”

Online customer-to-customer interactions, customer–firm affection, firm-loyalty and participation intention

“…Ang and Zaphiris (2010) argue that in the context of MMORPGs, users' activities may include a full range of collaborative activities. According to Park et al (2019), MMORPGs take a significant portion in the global market with 44.6 billion US dollars of market volume estimated by 2022. Provided the size of the market and the nature of the online multiplayer games that require collaboration and teamwork, it is critical to comprehend the impacts of customer-to-customer interactions in the context of MMORPG.…”

Understand Watchdogs: Discover How Game Bot Get Discovered

“…The LSTM operated well with the timeseries data in terms of computations of influences to the future situation. They proved the proposed method achieved high performance with over 95% accuracy (Park et al, 2019). Tsaur et al experimented with calculating the abnormal rate of each player and classified game bots through their proposed deep learning models.…”

“…In the early stage of the related research, people considered the game bots' characteristics a primary key. Reference Data Proposed Method (Kang et al, 2013) AION Rule set (Chung et al, 2013) Yulgang Online Multiple SVM (Lee et al, 2016) Lineage, AION, Blade & Soul Logistic Regression (Park et al, 2019) AION LSTM (Tsaur et al, 2019) KANO MLP NN (Tao et al, 2018) NetEase MMORPGs ABLSTM (Lee et al, 2018) Lineage Network Analysis visualizing the bot characters' network in a live service game, they proposed the analysis of transactions among game characters can be a significant pattern of game bot detection (Lee et al, 2018). Chung et al proposed a machine learning methodology to detect malicious activities.…”