ZAPs: A Reward Attribution Framework for DeFi Ecosystems with Adversarial-Robust Scoring via Parallel Anomaly Ensemble Detection

2026-07-30

AI Summary

This work addresses the vulnerability of DeFi incentive mechanisms to Sybil attacks and whale manipulation, stemming from the unfairness and lack of adversarial robustness in conventional reward distributions based on transaction volume or address counts. The authors propose the ZAPs framework, which quantifies users’ genuine economic contributions through intra-protocol percentile normalization and a two-tier ecological weighting scheme. ZAPs further introduces a four-layer defense system that innovatively integrates behavioral memory, graph clustering, and a parallel anomaly detection model—comprising one-class reconstruction, Isolation Forest ensembles, and transaction integrity verification—to identify malicious activities. Empirical evaluation demonstrates a ROC-AUC of 0.923 on real-world malicious data and a 30–90% reduction in reward extraction under simulated attacks. Post-deployment metrics show a 56% decrease in Sybil-driven allocations, a 49% increase in high-quality user participation, and a 50% reduction in sell-side pressure.

Abstract

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.

Problem

• DeFi
• reward attribution
• Sybil attacks
• adversarial robustness
• incentive manipulation

Innovation

• adversarial-robust scoring
• parallel anomaly ensemble
• reward attribution framework
• sybil resistance
• DeFi incentive design