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A Time Series Classification Approach to Game Bot Detection

Online games consumers are strongly grown in the last years attracted by the always higher quality of the games and the more effective gaming infrastructures. The increasing of on line games market is also concurrent to the diffusion of game bots that allow to automatize malicious tasks obtaining some rewards with respect to the other game players (the game bots user increases personal benefits and popularity with low effort). Given the interest of game developers to preserve game equity and player satisfaction, the topic of game bots detection is becoming very critical and consists to distinguish between game bots and human players behaviour. This paper describes an approach to the online role player games bot detection based on time series classification used to discriminate between human and game bots behavioral features. In this paper an application of the proposed approach in a real role player game is reported.
7th ACM International Conference on Web Intelligence, Mining and Semantics (WIMS), Amantea, Italy, 2017

Autori esterni: Mario Luca Bernardi (Università degli Studi Giustino Fortunato), Marta Cimitile (Università degli Studi di Roma Unitelma Sapienza)
Autori IIT:

Tipo: Contributo in atti di convegno
Area di disciplina: Computer Science & Engineering

File: time-series-class.pdf

Attività: Sicurezza di dispositivi mobili