发表机构
Zeru Finance; The Ohio State University（泽鲁金融; 俄亥俄州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
ZAPs是DeFi生态系统的奖励归因框架，结合经济贡献评分与四层防御栈，通过并行异常集成检测提升对抗鲁棒性，可减少女巫攻击、优化奖励分配并提升生态系统质量。
AI 中文摘要
激励计划是去中心化金融（DeFi）中用户获取的核心，但许多奖励系统依赖原始交易量、交易笔数和钱包数量，易受机器人（bot）和女巫（sybil）操作攻击。本文提出ZAPs——一种将经济贡献评分与对抗鲁棒性相结合的奖励归因框架。复合活动评分采用协议特定的百分位数归一化，以限制大户（whale）主导地位，同时保留用户间的差异化；双层加权机制结合了部门内的协议份额与生态系统内的部门份额，降低了小协议的刷取收益。研究表明，从任何协议可获得的最大奖励受限于该协议的全球交易量份额。ZAPs还引入了四层防御栈，包括交易级完整性检查、并行异常集成、分配后行为记忆以及基于图的女巫聚类。该异常集成结合了单类重构模型与孤立森林（isolation forest），并采用分级而非二元惩罚。在覆盖124638笔交易的1073个标记恶意钱包上，当孤立森林在良性钱包上训练时，该集成的ROC-AUC为0.923±0.013，而仅使用重构模型时为0.891±0.016；在合并总体上训练则会反转其极性并消除集成增益。受控模拟显示，对抗性奖励捕获减少了30%-90%，而合法用户场景变化为1%-8%。实际活动记录显示，女巫分配减少了56%，优质钱包参与度提高了49%，抛售压力降低了50%。
英文摘要
Incentive programs are central to user acquisition in decentralized finance, but many reward systems rely on raw volume, transaction count, and wallet count, making them vulnerable to bots and sybil operations. We present ZAPs, a reward attribution framework that combines economic contribution scoring with adversarial robustness. A composite activity score uses protocol-specific percentile normalization to limit whale dominance while preserving differentiation among users. A two-layer weighting mechanism combines protocol share within sector and sector share within the ecosystem, which reduces the profitability of farming small protocols. We show that the maximum reward obtainable from any protocol is bounded by that protocol's global volume share. ZAPs also introduces a four-layer defense stack consisting of transaction-level integrity checks, a parallel anomaly ensemble, post-distribution behavioral memory, and graph-based sybil clustering. The anomaly ensemble combines a one-class reconstruction model with an isolation forest and applies graduated rather than binary penalties. On 1,073 labeled malicious wallets covering 124,638 transactions, the ensemble achieves 0.923 +/- 0.013 ROC-AUC, compared with 0.891 +/- 0.016 for the reconstruction model alone, when the isolation forest is trained on benign wallets. Training it on the pooled population reverses its polarity and removes the ensemble gain. Controlled simulations reduce adversarial reward capture by 30-90 percent while legitimate-user scenarios change by 1-8 percent. Live campaigns recorded a 56 percent reduction in sybil allocation, a 49 percent increase in quality-wallet participation, and a 50 percent reduction in sell pressure.
Comments19 pages, 5 figures, 7 tables
