从“病毒式”到“虚空”：用于识别“ rug pull”的多维行为与合约分析
From Viral to Void: Multi-Dimensional Behavioral and Contractual Analysis for Rug Pull Identification
浏览论文内容
中文总结 AI 辅助
针对以太坊“rug pull”诈骗检测的缺陷，本文构建多维特征体系，设计结合SMOTE与Focal Loss的MLP模型，开发Flask可视化检测系统，实验指标优于传统方法。
中文摘要 AI 辅助
随着区块链与去中心化金融（DeFi）生态系统不断扩张与成熟，涉及模因币的“rug pull”诈骗发生频率日益提升，对投资者资产安全及行业健康发展构成威胁。“rug pull”诈骗具有部署成本极低、执行隐蔽、资金转移迅速、检测难度大等特征，传统人工审查或固定规则难以满足实时预警需求，现有检测方法普遍存在特征维度单一、类别不平衡处理不足、模型泛化性与可解释性弱等问题。为解决这些缺陷，本文聚焦基于以太坊的“rug pull”诈骗检测：首先，明确其定义、类型与危害机制，构建基于恶意智能合约设计、链上交易异常、流动性操纵、社交媒体披露等维度的多维特征体系；其次，以“Second Uncle Coin”（代币符号：BOBU）案例为例，重构攻击过程并推导定量检测指标；随后，设计基于多层感知机（MLP）的风险检测模型，采用SMOTE过采样与Focal Loss结合的策略解决样本不平衡问题，动态搜索最优阈值以平衡精确率与召回率，同时引入梯度剪枝与早停法提升训练稳定性；实验表明，该模型在测试集上的准确率达0.927、F1分数为0.787、AUC-ROC为0.952，表现优于传统方法；最后，利用Flask框架开发可视化的基于网页的检测系统，支持批量风险评估、高风险排序展示及结果导出功能。
英文摘要
As the blockchain and decentralized finance (DeFi) ecosystems continue to expand and mature, rug pull scams involving meme coins are occurring with increasing frequency, posing a threat to the security of investors' assets and the healthy development of the industry. Rug Pull scams are characterized by extremely low deployment costs, covert execution, rapid fund transfers, and high detection difficulty. Traditional manual reviews or fixed rules struggle to meet real-time early warning requirements, and existing detection methods generally suffer from issues such as a single feature dimension, inadequate handling of class imbalance, and weak model generalization and interpretability. To address these shortcomings, this paper focuses on the detection of Ethereum-based rug pull scams. First, we clarify their definitions, types, and harm mechanisms, and construct a multi-dimensional feature system based on dimensions such as malicious smart contract design, on-chain transaction anomalies, liquidity manipulation, and social media disclosures. Next, using the "Second Uncle Coin"(token symbol: BOBU) case as an example, we reconstruct the attack process and derive quantitative detection metrics. Subsequently, a risk detection model based on a Multi-Layer Perceptron (MLP) is designed. We employ a combined strategy of SMOTE oversampling and Focal Loss to address the issue of sample imbalance, dynamically search for optimal thresholds to balance precision and recall, and incorporate gradient pruning and early stopping to enhance training stability. Experiments show that the model achieves an accuracy of 0.927, an F1 score of 0.787, and an AUC-ROC of 0.952 on the test set, outperforming traditional methods. Finally, a visualizable web-based detection system is developed using the Flask framework, enabling batch risk assessment, high-risk ranking display, and result export functions.
