Anomaly Detection in eSport Games Through Periodical In-Game Movement Analysis with Deep Recurrent Neural Network

Mhd Irvan, Franziska Zimmer, Ryosuke Kobayashi, Maharage Perera, Roberta Tamponi, Rie Yamaguchi

2024

Abstract

Detecting anomaly in online video games is important to ensure a fair and secure gaming session. This is particularly crucial in eSport games, where competitive fairness is crucial. In this paper, we present an approach to anomaly detection in gaming sessions, using a variant of Deep Recurrent Neural Network, called Long Short-Term Memory (LSTM) network. Recurrent Neural Networks (RNNs) and their variant, LSTMs, are well-suited for this kind of task due to their ability to capture sequential patterns in gameplay data. The proposed system learns from normal gameplay patterns to identify anomalous behaviors such as impersonation. To confirm the feasibility of our approach, we use a game called Counter-Strike: Global Offensive (CSGO) serving as a case study. We utilize a public CSGO dataset containing in-game movement data, including coordinates, timestamps, and other contextual information. To test the model’s detection capabilities, synthetic data representing anomalous behaviors was injected into the dataset. The data was preprocessed and segmented into sequences, simulating the dynamics of player movements. Our LSTM model was trained to learn temporal dependencies within these sequences, enabling it to distinguish between normal and anomalous behaviors. Performance evaluation demonstrated the model’s robustness and effectiveness in detecting anomalies. The results indicate that our approach is able to detect anomalous activities, highlighting its potential for application in online gaming platforms to foster a more enjoyable gaming experience for all participants.

Paper Citation

in Harvard Style

Irvan M., Zimmer F., Kobayashi R., Perera M., Tamponi R. and Yamaguchi R. (2024). Anomaly Detection in eSport Games Through Periodical In-Game Movement Analysis with Deep Recurrent Neural Network. In Proceedings of the 16th International Joint Conference on Computational Intelligence - Volume 1: NCTA; ISBN 978-989-758-721-4, SciTePress, pages 430-437. DOI: 10.5220/0012978100003837

in Bibtex Style

@conference{ncta24,
author={Mhd Irvan and Franziska Zimmer and Ryosuke Kobayashi and Maharage Perera and Roberta Tamponi and Rie Yamaguchi},
title={Anomaly Detection in eSport Games Through Periodical In-Game Movement Analysis with Deep Recurrent Neural Network},
booktitle={Proceedings of the 16th International Joint Conference on Computational Intelligence - Volume 1: NCTA},
year={2024},
pages={430-437},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012978100003837},
isbn={978-989-758-721-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computational Intelligence - Volume 1: NCTA
TI - Anomaly Detection in eSport Games Through Periodical In-Game Movement Analysis with Deep Recurrent Neural Network
SN - 978-989-758-721-4
AU - Irvan M.
AU - Zimmer F.
AU - Kobayashi R.
AU - Perera M.
AU - Tamponi R.
AU - Yamaguchi R.
PY - 2024
SP - 430
EP - 437
DO - 10.5220/0012978100003837
PB - SciTePress