Sybil Detection
Identify coordinated bot networks and fake accounts using advanced clustering algorithms.
Overview
Sybil detection identifies coordinated bot networks and fake accounts by analyzing transaction patterns, funding sources, and behavioral similarities across multiple wallet addresses. This is critical for airdrop hunting, governance voting analysis, and fraud detection.
Detect airdrop farmers, identify fake volume, and analyze governance manipulation.
How It Works
FundTracer uses multiple detection methods to identify Sybil wallets:
- Collect a list of wallet addresses to analyze
- Fetch transaction history for each address
- Apply clustering algorithms to find related wallets
- Score each cluster based on suspicious patterns
- Generate a detailed report with findings
Detection Methods
Same-Block Detection
Identifies wallets that execute transactions in the same block, often indicating bots.
Funding Clustering
Groups wallets that share common funding sources.
Behavior Analysis
Identifies similar behavioral patterns across multiple wallets.
Similarity Scoring
Calculates similarity scores based on multiple factors.
Interpreting Results
Each wallet receives a risk score based on the analysis:
| Score Range | Risk Level | Description |
|---|---|---|
| 80-100 | Critical | High likelihood of Sybil activity |
| 60-79 | High Risk | Multiple suspicious indicators |
| 40-59 | Medium Risk | Some suspicious patterns detected |
| 0-39 | Low Risk | Minimal indicators of Sybil activity |