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This research introduces a scalable and application-agnostic framework for discovering persistent behavioral patterns in blockchain activity, addressing the limitations of existing methods that are often specific and hard to interpret. By constructing enriched behavior sentences and utilizing a two-step embedding process, the framework effectively captures both individual actions and user behavior over time. Evaluation on over 30 million Ethereum transactions reveals the framework's ability to identify stable patterns of both routine and malicious activities, significantly enhancing blockchain forensics and threat detection.
Uncovering stable behavioral patterns in blockchain transactions reveals both routine and malicious activities, transforming our approach to forensic analysis.
Public blockchain data enables large-scale DeFi-related analysis, but many existing approaches are application-specific, difficult to scale, or hard to interpret. This research proposes a scalable, application-agnostic framework for \emph{persistent behavioural pattern discovery} from large-scale blockchain activity. It constructs behaviour sentences enriched with contract, token and market context, then applies a two-step embedding process: sentence-level embeddings capture individual actions, while sequence-level embeddings capture user behaviour over time. An interpretable behavioural profiler characterizes discovered communities through behavioural motifs, routines, temporal dynamics, entity exposure, and suspiciousness evidence. Evaluation on Ethereum using over 30 million transactions shows that the framework uncovers both routine and malicious behavioural patterns, including decentralised exchange (DEX) trading, NFT activity, phishing, bot operations, oracle manipulation, and rug-pull schemes. Importantly, many patterns remain stable across independent observation windows, enabling the identification of long-term behaviours beyond a single analysis period. The proposed framework combines scalability, interpretability, and persistence analysis, supporting blockchain forensic investigation, behavioural attribution, and threat discovery.
