发表机构
Griffith University; Australian Catholic University（格里菲斯大学; 澳大利亚天主教大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出一种可扩展、应用无关的区块链行为模式发现框架，结合两步嵌入与可解释分析，可在以太坊超3000万笔交易中发现常规与恶意模式，支持区块链取证等应用。
AI 中文摘要
公共区块链数据支持大规模去中心化金融（DeFi）相关分析，但现有诸多方法存在应用特定性、难以扩展或可解释性差的问题。本研究提出一种可扩展、应用无关的框架，用于从大规模区块链活动中发现持久性行为模式。该框架构建包含合约、代币及市场上下文的行为语句，再应用两步嵌入过程：语句级嵌入捕捉个体行为，序列级嵌入捕捉用户随时间的行为；可解释行为分析器通过行为基序、常规模式、时间动态、实体暴露及可疑性证据表征所发现的社区。在以太坊上使用超3000万笔交易进行评估，结果显示该框架可发现常规及恶意行为模式，包括去中心化交易所（DEX）交易、非同质化代币（NFT）活动、钓鱼攻击、机器人操作、预言机操纵及 rug-pull （地毯式拉盘）骗局；重要的是，许多模式在独立观测窗口间保持稳定，可识别超出单一分析周期的长期行为。该框架兼具可扩展性、可解释性与持久性分析能力，支持区块链取证调查、行为归因及威胁发现。
英文摘要
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.
