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Who Plays to Earn? Detecting Bots in Web3 Games via On-Chain Transactions Analysis

Authors Woncheol Jeong , Sohee Choi, Jiwon Yoon

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Document Identifiers

Subject Classification

ACM Subject Classification
  • Security and privacy → Intrusion/anomaly detection and malware mitigation
  • Computing methodologies → Neural networks
  • Information systems → Data mining
Keywords
  • Bot detection
  • Web3 gaming
  • Play-to-Earn
  • on-chain behavioral analysis
  • transaction graph analysis
  • heterogeneous graph neural network

Metrics

Abstract

Bots in Web3 Play-to-Earn (P2E) games extract on-chain rewards at scale, accelerating token inflation and eroding the incentives that sustain legitimate participation. Existing defenses transfer poorly to this setting. Traditional game-bot detectors require proprietary server-side telemetry that is unavailable on permissionless chains, and blockchain-native detectors target DeFi behaviors whose footprints differ fundamentally from those of game automation; research specific to Web3 game bots remains scarce, and no public labeled dataset exists. We argue that the very economic asymmetry that makes P2E automation profitable also exposes it, because the temporal, topological, financial, and behavioral regularities that bots cannot disguise without forfeiting their efficiency advantage remain observable from public on-chain transactions alone. Building on this insight, we propose a cross-game, cross-chain detection framework that models player activity as a heterogeneous temporal transaction graph over a unified five-type edge vocabulary, extracts 32 chain-agnostic behavioral features, and classifies accounts through a dual-branch encoder fusing a typed-edge graph attention network (GAT) with a gated recurrent unit (GRU)-attention sequence model, bootstrapped by graph-contrastive pre-training. To support evaluation, we further release the first multi-game labeled bot dataset, spanning three P2E titles on three heterogeneous chains with 57,879 accounts and 3,551 confirmed bots. On the joint test split, the framework attains F1 = 94.39%, AUC = 99.92%, and AP = 98.90%, with a sharply bimodal score distribution and stable performance across a wide range of decision thresholds.

Cite As Get BibTex

Woncheol Jeong, Sohee Choi, and Jiwon Yoon. Who Plays to Earn? Detecting Bots in Web3 Games via On-Chain Transactions Analysis. In 8th Conference on Advances in Financial Technologies (AFT 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 395, pp. 32:1-32:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026) https://doi.org/10.4230/LIPIcs.AFT.2026.32

Author Details

Woncheol Jeong
  • Korea University, Seoul, Republic of Korea
Sohee Choi
  • Korea University, Seoul, Republic of Korea
Jiwon Yoon
  • Korea University, Seoul, Republic of Korea

Funding

This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No. RS-2024-00460321, Development of Digital Asset Transaction Tracking Technology to Prevent Malicious Financial Conduct in the Digital Asset Market). This work was also supported by Korea Internet & Security Agency(KISA) grant funded by the Korea government(PIPC) (No. 2780000036, Masters and Doctoral Program for Advanced Talent Development in Personal Data Protection and Responsible Use).

Supplementary Materials

References

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