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The paper introduces TEMG-TTA, a novel graph anomaly detection framework designed to address adversarial pattern evolution and out-of-distribution (OOD) challenges in blockchain transaction anomaly detection. TEMG-TTA captures 3-node temporal motif distributions for each active address and employs a test-time adaptation strategy to share common patterns between training and testing graphs. Experiments on five real-world datasets demonstrate that TEMG-TTA outperforms state-of-the-art graph anomaly detection approaches by an average of 54.88%.
Blockchain anomaly detection gets a massive boost: TEMG-TTA leverages temporal motifs and test-time adaptation to achieve a 54.88% performance leap over existing methods.
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}mporal \textbf{M}otif-aware \textbf{G}raph \textbf{T}est-\textbf{T}ime \textbf{A}daptation (\textbf{TEMG-TTA}). First, we comprehensively capture the 3-node temporal motif distribution of each active address using an efficient computational mechanism, enabling downstream temporal motif-aware graph learning. Second, we design a simple yet effective test-time adaptation strategy to facilitate the sharing of common patterns between training and testing graphs. Extensive experiments on 5 real-world datasets demonstrate that our proposed \textbf{TEMG-TTA} outperforms \textit{state-of-the-art} GAD approaches by an average of 54.88\%. A further case study on interpretable motif patterns reveals that \textbf{TEMG-TTA} explicitly characterizes the complex transaction patterns of anomalous addresses, thereby verifying the effectiveness of our technical designs. Our code will be made publicly available https://github.com/LuoXishuang0712/TEMG-TTA/.
