区块链分析中的决策建模：基于泄漏感知的树模型与序列模型评估
Modeling Decisions in Blockchain Analytics: A Leakage-Aware Evaluation of Tree-Based vs. Sequential Models
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中文总结 AI 辅助
该研究针对以太坊Sybil机器人检测的标签泄漏问题，提出泄漏感知检测方法，对比序列模型与树模型，发现XGBoost在性能、延迟及能耗上均优于Transformer序列模型。
中文摘要 AI 辅助
Sybil机器人是以太坊上模仿合法用户以提取空投奖励或影响治理的实体。近期Sybil检测方法日益采用深度学习，将区块链活动视为类语言序列，但复杂序列模型在实时监控中计算成本高，且报告性能可能因高信号智能合约的标签泄漏而虚高。本文研究：有机用户、Sybil机器人与MEV机器人的交易历史结构复杂度是否存在差异；减少泄漏后序列模型是否优于树型表格模型；交易顺序或时间哪个提供更强行为信号；所得模型是否适用于低延迟部署。本文提出的泄漏感知Sybil机器人检测方法包含盲点击协议和钱包行为的交易语法表示，前者消除高信号合约相关捷径，后者用节奏、EVM执行结构与意图建模钱包。在以太坊实体分类任务中，将Transformer、BiLSTM序列模型与XGBoost、SVM基线对比评估该方法，贡献泄漏感知以太坊实体分类框架与交易语法表示。结果表明，在泄漏感知评估下，XGBoost性能优于Transformer序列模型，且延迟更低、预估能耗更少。
英文摘要
Sybil bots are Ethereum actors that imitate legitimate users to extract airdrop rewards or influence governance. Recent Sybil detection methods increasingly use deep learning and treat blockchain activity as a quasi-linguistic sequence. However, complex sequence models are computationally expensive for real-time monitoring, and their reported performance may be inflated by label leakage from high-signal smart contracts. We ask whether and how organic users, Sybil bots, and MEV bots differ in the structural complexity of their transaction histories; whether sequential models outperform tree-based tabular models once leakage is reduced; whether transaction order or timing provides the stronger behavioral signal; and whether the resulting models are practical for low-latency deployment. Our approach to leakage-aware Sybil bot detection consists of a Blind-Spot protocol and a Transaction Grammar representation of wallet behavior. The former eliminates shortcuts associated with high-signal contracts, whereas the latter models wallets using rhythm, EVM execution structure, and intent. We evaluate this approach on Ethereum actor classification by comparing Transformer and BiLSTM sequence models against XGBoost and SVM baselines. We contribute a framework for leakage-aware Ethereum actor classification and a Transaction Grammar representation of wallet behavior. Our results demonstrate that, under leakage-aware evaluation, XGBoost outperforms Transformer-based sequence models while providing lower latency and estimated energy use.
