DeFiFusion：结合交易事件与智能合约检测价格操纵攻击
DeFiFusion: Combining Transaction Events with Smart Contracts to Detect Price Manipulation Attacks
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中文总结 AI 辅助
DeFiFusion提出双模态融合框架，结合交易事件与智能合约语义，利用LLM提取合约逻辑和Transformer捕获循环执行结构，实现价格操纵攻击的高精度检测。
中文摘要 AI 辅助
去中心化金融（DeFi）已作为一种快速增长的基于区块链的金融服务而兴起，其中市场交易动态与底层智能合约逻辑错综复杂地交织在一起。这种自主交互在消除中心化中介的同时，显著扩大了DeFi协议遭受价格操纵攻击（PMAs）的漏洞面，此类攻击已造成灾难性的财务损失。尽管问题严重，现有检测范式仍存在根本性局限。以交易为中心的方法缺乏对合约执行语义的感知，在合法市场波动下容易产生误报；而静态合约分析忽略实际交易行为，经常报告在实践中无法利用的漏洞。我们提出DeFiFusion，一种双模态PMA检测框架，通过在统一流程中联合建模交易事件和智能合约语义来弥合这一差距。我们的核心洞察是，PMA的恶意性仅源于交易行为与其所利用的合约逻辑之间的交互；单独任一信号都不足以判断。据此，我们推导出价格操纵感知的事件编码，以提取针对操纵模式定制的细粒度时间与经济特征。我们进一步引入基于LLM的合约语义提取，以提供先前行为方法所缺乏的执行逻辑上下文。为融合这些模态，我们提出一种带有T5风格相对位置编码的双模态投影融合Transformer，捕获区分PMA与良性市场活动的循环多阶段执行结构。大量实验表明，DeFiFusion持续实现最先进的检测性能，有效召回225个PMA案例中的222个，同时保持96.10%的精确率。
英文摘要
Decentralized Finance (DeFi) has emerged as a rapidly growing blockchain-based financial service, where market transaction dynamics and underlying smart contract logic are intricately intertwined. This autonomous interplay, while eliminating centralized intermediaries, significantly expands the vulnerability surface of DeFi protocols to Price Manipulation Attacks (PMAs), which have already inflicted catastrophic financial losses. Despite their gravity, existing detection paradigms suffer from fundamental limitations. Transaction-centric methods lack awareness of contract execution semantics, making them prone to false positives under legitimate market volatility, while static contract analyses ignore real transaction behaviors and frequently report vulnerabilities that are infeasible to exploit in practice. We present DeFiFusion, a dual-modal PMA detection framework that closes this gap by jointly modeling transaction events and smart contract semantics within a unified pipeline. Our core insight is that PMA maliciousness emerges only from the interaction between transaction behaviors and the contract logic they exploit; neither signal suffices in isolation. Accordingly, we derive price-manipulation-aware event encoding for extracting fine-grained temporal and economic features tailored to manipulation patterns. We further introduce LLM-based contract semantic extraction to supply the execution-logic context that prior behavioral methods lack. To fuse these modalities, we propose a Dual-Modal Projection-Fusion Transformer with T5-style relative positional encoding, capturing the cyclic multi-stage execution structures that distinguish PMAs from benign market activity. Extensive experiments demonstrate that DeFiFusion consistently achieves state-of-the-art detection performance, effectively recalling 222 of the 225 PMA cases while maintaining a precision of 96.10%.
发表机构
- Nanjing University of Aeronautics and Astronautics（南京航空航天大学）
- National University of Singapore（新加坡国立大学）
- Zhejiang University of Technology（浙江工业大学）
- Zhejiang University（浙江大学）
- Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security（杭州高新区（滨江）区块链与数据安全研究院）
机构由 AI 辅助整理，请以论文原文为准。
