捍卫锚定：DeFi稳定币的实时动态防护与异常检测
Defending the Peg: Real-Time Dynamic Protection and Anomaly Detection in DeFi Stablecoins
AI总结：
针对DeFi稳定币智能合约安全问题，本文分析真实攻击事件，提出实时动态防御架构，构建基于Bi-LSTM的异常检测模型，实现高准确率、高召回率与低延迟的检测效果。
AI中文摘要：
随着去中心化金融（Decentralized Finance，DeFi）生态系统的快速发展，稳定币已成为连接加密货币市场与传统金融范式的关键基础设施。然而，稳定币系统高度依赖智能合约执行自动化操作，这些系统部署后具有不可变的特性，意味着安全漏洞被利用会造成不可逆的巨额经济损失，并可能引发系统性金融风险。当前稳定币智能合约安全研究面临缺乏针对性领域目标、静态防御模型过时等挑战。为解决上述问题，本文系统分析了稳定币环境中的常见攻击向量，提出了一种实用的实时动态防御架构。通过分析12起真实安全事件，阐明了重入攻击、预言机操纵、复合闪电贷攻击等高风险模式的底层机制；同时，构建了利用多维链上时序特征与Bi-LSTM算法的实时异常检测模型。实验结果表明，该模型分类准确率达96.61%，恶意攻击样本平均召回率为97.70%，单次推理延迟在1.5至2.8毫秒之间。
英文摘要：
With the rapid evolution of the Decentralized Finance (DeFi) ecosystem, stablecoins have emerged as a critical infrastructure bridging the cryptocurrency market with traditional financial paradigms. However, stablecoin systems rely heavily on smart contracts to execute automated operations. The immutable nature of these systems post-deployment means that the exploitation of security vulnerabilities can lead to irreversible, massive economic losses and potentially trigger systemic financial risks. Current research on stablecoin smart contract security faces challenges such as a lack of domain-specific targeting and the obsolescence of static defense models. To address this, this paper systematically analyzes common attack vectors in stablecoin environments and proposes a practical, real-time dynamic defense architecture. By analyzing 12 real-world security incidents, we elucidate the underlying mechanisms of high-risk patterns such as reentrancy attacks, oracle manipulation, and composite flash loan attacks. Concurrently, we construct a real-time anomaly detection model utilizing multi-dimensional on-chain temporal features and the Bi-LSTM algorithm. Experimental results demonstrate that this model achieves a classification accuracy of 96.61\%, with an average recall rate of 97.70\% for malicious attack samples, and a single inference latency ranging from 1.5 to 2.8 milliseconds.
