用语义图和自反解释法，精准识别攻击性智能合约并说明原因。
SEASONED: Semantic-Enhanced Self-Counterfactual Explainable Detection of Adversarial Exploiter Contracts
- 通过字节码构建语义关系图，捕捉合约逻辑依赖。
- 自反解释检测器在359个攻击合约上准确率达98.7%。
- 生成可读的攻击逻辑解释，适合安全审计与研究者使用。
去中心化金融（DeFi）攻击导致重大损失，常由攻击性智能合约（AECs）利用目标合约漏洞发起。为提前识别此类威胁，本文提出可解释的AEC检测框架SEASONED。现有方法难以捕捉语义依赖且缺乏可解释性，限制了分析效果。SEASONED从合约字节码提取语义信息，构建语义关系图（SRG），并采用自反解释检测器（SCFED）对SRG进行分类，生成突出核心攻击逻辑的解释。SCFED通过从解释中提取代表性信息，进一步提升鲁棒性、泛化能力与数据效率。理论分析与实验结果表明，SEASONED在检测性能、鲁棒性、泛化性及数据效率方面表现优异。为支持后续研究，本文还发布了包含359个AEC的新数据集。
原文摘要 · Abstract (English)
Decentralized Finance (DeFi) attacks have resulted in significant losses, often orchestrated through Adversarial Exploiter Contracts (AECs) that exploit vulnerabilities in victim smart contracts. To proactively identify such threats, this paper targets the explainable detection of AECs. Existing detection methods struggle to capture semantic dependencies and lack interpretability, limiting their effectiveness and leaving critical knowledge gaps in AEC analysis. To address these challenges, we introduce SEASONED, an effective, self-explanatory, and robust framework for AEC detection. SEASONED extracts semantic information from contract bytecode to construct a semantic relation graph (SRG), and employs a self-counterfactual explainable detector (SCFED) to classify SRGs and generate explanations that highlight the core attack logic. SCFED further enhances robustness, generalizability, and data efficiency by extracting representative information from these explanations. Both theoretical analysis and experimental results demonstrate the effectiveness of SEASONED, which showcases outstanding detection performance, robustness, generalizability, and data efficiency learning ability. To support further research, we also release a new dataset of 359 AECs.
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