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Early Rug Pull Warning for BSC Meme Tokens via Multi-Granularity Wash-Trading Pattern Profiling

Dingding Cao, Bianbian Jiao, Jingzong Yang, Yujing Zhong, Wei Yang

Abstract

The high-frequency issuance and short-cycle speculation of meme tokens in decentralized finance (DeFi) have significantly amplified rug-pull risk. Existing approaches still struggle to provide stable early warning under scarce anomalies, incomplete labels, and limited interpretability. To address this issue, an end-to-end warning framework is proposed for BSC meme tokens, consisting of four stages: dataset construction and labeling, wash-trading pattern feature modeling, risk prediction, and error analysis. Methodologically, 12 token-level behavioral features are constructed based on three wash-trading patterns (Self, Matched, and Circular), unifying transaction-, address-, and flow-level signals into risk vectors. Supervised models are then employed to output warning scores and alert decisions. Under the current setting (7 tokens, 33,242 records), Random Forest outperforms Logistic Regression on core metrics, achieving AUC=0.9098, PR-AUC=0.9185, and F1=0.7429. Ablation results show that trade-level features are the primary performance driver (Delta PR-AUC=-0.1843 when removed), while address-level features provide stable complementary gain (Delta PR-AUC=-0.0573). The model also demonstrates actionable early-warning potential for a subset of samples, with a mean Lead Time (v1) of 3.8133 hours. The error profile (FP=1, FN=8) indicates that the current system is better positioned as a high-precision screener rather than a high-recall automatic alarm engine. The main contributions are threefold: an executable and reproducible rug-pull warning pipeline, empirical validation of multi-granularity wash-trading features under weak supervision, and deployment-oriented evidence through lead-time and error-bound analysis.

Early Rug Pull Warning for BSC Meme Tokens via Multi-Granularity Wash-Trading Pattern Profiling

Abstract

The high-frequency issuance and short-cycle speculation of meme tokens in decentralized finance (DeFi) have significantly amplified rug-pull risk. Existing approaches still struggle to provide stable early warning under scarce anomalies, incomplete labels, and limited interpretability. To address this issue, an end-to-end warning framework is proposed for BSC meme tokens, consisting of four stages: dataset construction and labeling, wash-trading pattern feature modeling, risk prediction, and error analysis. Methodologically, 12 token-level behavioral features are constructed based on three wash-trading patterns (Self, Matched, and Circular), unifying transaction-, address-, and flow-level signals into risk vectors. Supervised models are then employed to output warning scores and alert decisions. Under the current setting (7 tokens, 33,242 records), Random Forest outperforms Logistic Regression on core metrics, achieving AUC=0.9098, PR-AUC=0.9185, and F1=0.7429. Ablation results show that trade-level features are the primary performance driver (Delta PR-AUC=-0.1843 when removed), while address-level features provide stable complementary gain (Delta PR-AUC=-0.0573). The model also demonstrates actionable early-warning potential for a subset of samples, with a mean Lead Time (v1) of 3.8133 hours. The error profile (FP=1, FN=8) indicates that the current system is better positioned as a high-precision screener rather than a high-recall automatic alarm engine. The main contributions are threefold: an executable and reproducible rug-pull warning pipeline, empirical validation of multi-granularity wash-trading features under weak supervision, and deployment-oriented evidence through lead-time and error-bound analysis.
Paper Structure (15 sections, 1 equation, 7 figures, 6 tables)

This paper contains 15 sections, 1 equation, 7 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Method
  3. Overview
  4. Data Collection, Cleaning, and Labeling
  5. Multi-granularity Wash-trading Feature Construction
  6. Early-warning Modeling and Inference
  7. Ablation and Error-analysis Design
  8. Reproducibility and Implementation Details
  9. Results and Discussion
  10. Dataset Construction and Pattern-feature Results
  11. Main Performance Comparison
  12. Ablation Analysis and Feature-group Contributions
  13. Error Profile and Case Timeline Analysis
  14. Limitations
  15. Conclusion

Figures (7)

  • Figure 1: BSC Meme Token Rug Pull Early-warning Framework. The overall pipeline contains four stages (E1--E4): data construction and labeling, wash-trading pattern profiling, early-warning modeling, and ablation/error analysis.
  • Figure 2: Data Collection and Labeling Pipeline. The workflow includes export, token-wise merging, deduplication, normalization, window capping, rule-based labeling, and quality-control checks.
  • Figure 3: Wash-trading Pattern Profiling and Risk Vector Construction. Transaction-, address-, and flow-level signals are scored into Self/Matched/Circular components and then aggregated into token-level risk vectors.
  • Figure 4: Early-warning Modeling and Deployment Flow. The upper lane shows model training and evaluation. The lower lane shows inference and alert triggering, with lead-time estimation as deployment output.
  • Figure 5: Main Performance Comparison between Logistic Regression and Random Forest. (a) Classification metrics. (b) Ranking metrics.
  • ...and 2 more figures