arXiv:2605.29526v1 Announce Type: cross Abstract: Ever-evolving transaction patterns have significantly hindered anomaly detection on emerging cryptocurrency blockchains due to the vast number of addresses and diverse anomalous behaviors. Recently, advanced Graph Anomaly Detection (GAD) approaches applied to blockchains have faced two critical challenges: \textit{adversarial pattern evolution by malicious actors} and \textit{the out-of-distribution (OOD) problem caused by varied transaction semantics on blockchains}. To address these challenges, we propose a novel framework termed \textbf{TE}m
The increasing sophistication of malicious actors and the evolving nature of decentralized finance necessitate advanced anomaly detection methods for blockchain security.
Improved anomaly detection on blockchains enhances the integrity and security of the digital asset ecosystem, which is critical for broader adoption and systemic stability.
The proposed 'TEm' framework offers a new approach to combat 'adversarial pattern evolution' and 'out-of-distribution problems' in blockchain anomaly detection, potentially making these systems more robust.
- · Blockchain platforms
- · Cryptocurrency users
- · Cybersecurity firms
- · AI/ML researchers
- · Malicious actors (hackers, fraudsters)
- · Less sophisticated anomaly detection systems
Reduced financial losses due to blockchain hacks and scams.
Increased trust and regulatory acceptance for decentralized financial applications.
Acceleration of institutional investment into crypto assets due to perceived improved security and reliability.
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Read at arXiv cs.LG