Sybil Detection Model Writeup: GPU-Accelerated Multi-Chain Analysis

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Sybil Detection Model Report: GPU-Accelerated Multi-Chain Analysis

Author: Casuwyt Periay
Date: December 2024

1. Introduction

1.1. Problem Statement & Motivation

Sybil attacks pose a fundamental threat to decentralized systems in the Web3 ecosystem. These attacks involve malicious actors creating multiple fake identities (wallet addresses) to gain disproportionate influence, exploit airdrop distributions, manipulate governance decisions, or extract unfair rewards from protocol incentives. The detection of Sybil accounts is crucial for maintaining the integrity and fairness of blockchain networks. This project develops a machine learning solution to identify potential Sybil addresses based on their on-chain behavioral patterns across multiple blockchain networks.

1.2. Technical Approach

This analysis implements a GPU-accelerated machine learning pipeline leveraging RAPIDS (cuDF and cuPy) for efficient processing of large-scale blockchain data. The approach combines data from two major networks (Ethereum and Base) to create a comprehensive behavioral profile for each address, enabling robust Sybil detection across different blockchain ecosystems.

1.3. Methodology Overview

The project follows a systematic approach:

• GPU-Accelerated Data Processing: Utilizing NVIDIA L4 GPU with RAPIDS for fast data loading and feature extraction
• Multi-Chain Analysis: Combining behavioral data from Ethereum and Base networks
• Comprehensive Feature Engineering: Creating 24 behavioral features capturing transaction, token, and DEX interaction patterns
• Ensemble Modeling: Employing XGBoost and LightGBM with careful hyperparameter tuning
• Robust Validation: Implementing proper train-test splitting and cross-validation strategies

2. Data Description and Preparation

2.1. Dataset Overview

The analysis utilized comprehensive blockchain data from two networks:

Ethereum Network:

• Training addresses: 52,501 (with 2,516 Sybils)
• Test addresses: 20,369
• Transactions, token transfers, and DEX swap data

Base Network:

• Training addresses: 51,515 (with 1,515 Sybils)
• Test addresses: 20,369
• Similar transaction and activity data

Combined Dataset:

• Total training samples: 99,008 (after removing 59 potential false negatives)
• Sybil rate: 2.57% (2,526 Sybils out of 99,008 addresses)
• Class imbalance ratio: approximately 38:1 (Normal:Sybil)

2.2. Data Quality Enhancements

Several data quality measures were implemented:

• Numeric Data Cleaning: Handling of infinite values and extreme outliers
• Temporal Data Processing: Converting timestamps to Unix format for better model compatibility
• Address Normalization: Converting all addresses to lowercase for consistency
• False Negative Removal: Excluding 59 addresses identified as potential mislabeled samples

3. Feature Engineering

3.1. Feature Categories

The feature engineering process resulted in 24 carefully crafted features across five main categories:

1. Transaction Features (12 features)

• Basic counts: tx_sent_count, tx_received_count, tx_total_count
• Value statistics: tx_sent_value_sum, tx_sent_value_mean, tx_sent_value_std, tx_sent_value_max
• Network metrics: unique_to_addresses
• Temporal features: tx_first_timestamp_unix, tx_last_timestamp_unix, tx_days_active

2. Token Transfer Features (8 features)

• Activity metrics: token_sent_count, token_received_count, token_total_count
• Value analysis: token_sent_usd_sum, token_sent_usd_mean, token_sent_usd_max
• Diversity metrics: unique_tokens_sent, unique_symbols_sent

3. DEX Interaction Features (4 features)

• dex_swap_count
• dex_volume_in_usd
• dex_avg_swap_size_usd
• unique_dex_platforms

3.2. Feature Importance Analysis

The most influential features identified by the models were:

Top 5 Features:

1. tx_first_timestamp_unix (importance: 158.59) - Account creation time
2. tx_last_timestamp_unix (importance: 124.52) - Recent activity indicator
3. unique_to_addresses (importance: 69.05) - Network interaction breadth
4. tx_sent_value_max (importance: 67.51) - Maximum transaction value
5. tx_sent_value_sum (importance: 66.03) - Total value transferred

4. Model Development and Results

4.1. Model Architecture

Two gradient boosting models were employed:

XGBoost Configuration:

• GPU acceleration enabled
• 125 boosting rounds
• Custom objective for binary classification
• Scale position weight: 38.2 (to handle class imbalance)

LightGBM Configuration:

• GPU training with OpenCL
• Early stopping after 73 iterations
• Binary objective with class weight balancing
• Feature histogram optimization with 256 bins

4.2. Performance Metrics

The models achieved strong performance in identifying Sybil addresses:

Training Performance:

• XGBoost Validation AUC: 0.9965
• LightGBM Validation AUC: 0.9960

Test Set Predictions:

• Total test addresses: 20,369
• Predicted Sybils (>0.9 probability): 103 (0.51%)
• High-risk addresses (>0.8 probability): 326 (1.60%)
• Low-risk addresses (<0.2 probability): 14,734 (72.33%)

4.3. Prediction Distribution Analysis

The model produced well-separated predictions:

• Mean prediction score: 0.163
• Median prediction score: 0.050
• Standard deviation: 0.226
• Skewness: 1.568 (indicating right-skewed distribution)
• Kurtosis: 1.400 (moderate peakedness)

5. Key Findings and Insights

5.1. Behavioral Patterns of Sybil Accounts

1. Temporal Characteristics: Sybil accounts show distinct temporal patterns, with account age and activity timing being the most predictive features
2. Transaction Behavior: Sybils tend to have:

• Higher transaction volumes and values
• More diverse interaction patterns (unique addresses)
• Larger maximum transaction values

3. Network Effects: The breadth of network interactions (unique addresses contacted) is a strong indicator of Sybil behavior
   
   

5.2. Feature Category Importance

Analysis of feature categories revealed:

• Transaction features: 40.4% of total importance
• Value-based features: 22.8% of total importance
• Time-based features: 16.1% of total importance
• Token features: 14.3% of total importance
• DEX features: 6.3% of total importance
  
  

6. Technical Implementation Details

6.1. GPU Acceleration Benefits

• Hardware: NVIDIA L4 GPU
• Memory efficiency: Maintained under 2.1GB CPU memory usage
• Processing speed: Feature extraction completed in 0.08 minutes
• Scalability: Successfully processed over 100k addresses with millions of associated transactions

6.2. Model Optimization Strategies

1. Class Imbalance Handling: Used scale_pos_weight parameter (38.2) to properly weight minority class
2. Feature Scaling: Standardized features using scikit-learn’s StandardScaler
3. Outlier Management: Implemented quantile-based clipping for extreme values
4. Missing Value Strategy: Filled NaN values with 0 after careful analysis

7. Conclusions and Recommendations

7.1. Model Performance Summary

The developed Sybil detection system demonstrates strong performance with:

• High discriminative power (AUC > 0.996)
• Effective identification of high-risk addresses
• Well-calibrated probability outputs
• Efficient GPU-accelerated processing

7.2. Practical Applications

The model can be deployed for:

1. Airdrop Protection: Screening addresses before token distributions
2. Governance Security: Identifying potential Sybil voters
3. Risk Assessment: Continuous monitoring of network participants
4. Cross-chain Analysis: Detecting Sybils operating across multiple networks

7.3. Future Improvements

Potential enhancements include:

1. Graph-based Features: Incorporating network topology analysis
2. Behavioral Sequences: Analyzing temporal patterns of actions
3. Cross-chain Correlation: Linking addresses across more networks
4. Real-time Detection: Implementing streaming analysis capabilities

7.4. Limitations and Considerations

1. Label Quality: Model performance depends on the accuracy of training labels
2. Temporal Bias: Features may be less effective for newly created addresses
3. Adversarial Adaptation: Sybil operators may modify behavior to evade detection
4. False Positive Impact: ~27.7% of addresses have probabilities between 0.1-1.0, requiring careful threshold selection

8. Final Remarks

This GPU-accelerated Sybil detection system successfully identifies suspicious addresses with high accuracy while maintaining computational efficiency. The combination of comprehensive feature engineering, robust model selection, and careful validation produces a practical solution for enhancing security in decentralized systems. The model’s strong performance, particularly in identifying temporal and transactional patterns, provides a solid foundation for protecting blockchain ecosystems from Sybil attacks.