EVM Sleuth is an advanced blockchain transaction visualization tool designed to help users analyze and track transactions across Ethereum and other EVM-compatible chains. By leveraging interactive graph-based analytics, it enables security researchers, compliance teams, and blockchain enthusiasts to gain deeper insights into transaction flows.
This tool identifies various patterns and behaviors in blockchain transactions, including suspicious activities linked to hacks, fraud, and money laundering schemes. EVM Sleuth simplifies complex transaction networks, providing users with an intuitive interface for tracing fund movements between centralized exchanges (CEXs), decentralized exchanges (DEXs), smart contracts, bridges, and mixers.
EVM Sleuth is a comprehensive transaction analysis tool that provides real-time monitoring, automated pattern detection, and an easy-to-use visual interface. It helps users:
Track cryptocurrency movement across multiple chains.
Identify suspicious transaction patterns.
Classify addresses and categorize entity types.
Perform risk assessments and compliance checks.
Collaborate with teams to conduct deeper investigations.
| Feature | Description | Icon |
|---|---|---|
| Multi-chain Tracking | Track across different blockchains | 🔄 |
| Real-time Visualization | Live transaction monitoring | 📊 |
| Entity Recognition | Automatic address classification | 🏷️ |
| 1Million+ Pattern Detection | Identify suspicious patterns | 🔍 |
| Collaboration Tools | Team investigation features | 👥 |
Hack Investigations – Helps trace stolen funds, revealing the movement of assets through bridges, mixers, and wallets.
Fraud Detection – Identifies fraudulent transactions and deceptive trading strategies.
Asset Recovery – Aids in tracking lost or stolen assets and their subsequent movements.
Threat Monitoring – Enables real-time monitoring of high-risk transactions and addresses, detecting patterns with over 1 million+ filters.
AML Monitoring – Assists in detecting anti-money laundering violations and suspicious fund movements.
Risk Assessment – Provides an address-based risk score to assess transaction legitimacy.
Regulatory Reporting – Generates reports for financial regulators and law enforcement agencies.
Due Diligence – Supports background checks on wallet addresses and transaction histories.
┌──────────────────────────┐
│ Node Types │
├──────────────┬───────────┤
│ CEX │ 🟣 │
│ DEX │ 🔵 │
│ Bridge │ 🟢 │
│ Mixer │ 🔴 │
│ Regular │ ⚫ │
│ Tracking │ 🟡 │
└──────────────┴───────────┘
1️⃣ Enter Address/Transaction
↓
2️⃣ Select Chain
↓
3️⃣ Set Time Range
↓
4️⃣ Apply Filters
↓
5️⃣ Analyze Results
┌────────────────────────────────────────────────────────────────────┐
│ Tool Bar │
├────────────────────┬──────────────────────────────────────────────┤
│ │ │
│ Address Panel │ Canvas │
│ │ (Visuals, Graphs, Insights) │
│ │ │
│ Address Analyze │ Address Details / Monitor Address / Track │
│ │ Transactions │
├────────────────────┴──────────────────────────────────────────────┤
│ Status Bar │
└────────────────────────────────────────────────────────────────────┘
Transaction Types
Regular transfers
Contract interactions
Internal transactions
Token transfers
Visual Properties
Edge properties:
- Direction: arrow indicating flow
- Weight: thickness by value
- Style: pattern by type
- Color: status/chain
| Pattern | Visual | Description |
|---|---|---|
| Linear | →→→ | Sequential transfers |
| Split | ⑂ | One to many |
| Merge | ⋎ | Many to one |
| Cycle | ⟲ | Circular flow |
Risk Level:
🟢 Low (0-3)
🟡 Medium (4-7)
🔴 High (8-10)
Export Formats = {
"Visual": {
"SVG": "Vector Graphics",
"PNG": "High Resolution Image",
"PDF": "Report Format"
},
"Data": {
"CSV": "Spreadsheet",
"JSON": "API Format",
"XML": "Structured Data"
}
}
╔════════════════════════════════╗
║ Search Bar ║
╠════════════════════════════════╣
║ 📊 🔍 💾 ⚙️ ║
║ Graph Search Save Settings ║
╠════════════════════════════════╣
║ ║
║ Visualization ║
║ Area ║
║ ║
╠════════════════════════════════╣
║ Transaction Log ║
╚════════════════════════════════╝
/* Primary Colors */
.tracking { color: #FFB700; } /* 🟡 */
.dex { color: #4F46E5; } /* 🔵 */
.cex { color: #7C3AED; } /* 🟣 */
.bridge { color: #059669; } /* 🟢 */
.mixer { color: #DC2626; } /* 🔴 */
.default { color: #1F2937; } /* ⚫ */
┌─────────────┐
│ Search │
├─────────────┤
│ │
│ Graph │
│ View │
│ │
├─────────────┤
│ Controls │
└─────────────┘
Filters = {
🕒 Time Range
💰 Value Range
🔗 Chain Type
📝 Transaction Type
}
Transaction_Monitoring = {
"Data_Points": {
"xxxx": {
"xxxx",
"xxxx",
"xxxx",
"xxxx"
},
"Transaction_Data": {
"xxxx",
"xxxx",
"xxxx",
"xxxx",
"xxxx"
},
"State_Changes": {
"balance_changes",
"storage_changes",
"token_transfers"
}
"Hidden Mathod": {
"xxxxxx",
"xxxx",
"xxxx"
},
"Monitoring_Rate": "~15 TPS per chain"
}
# Data Processing Pipeline
1. Raw Transaction Ingestion
2. Event Log Parsing
3. Internal Transaction Reconstruction
4. Token Transfer Tracking
5. Cross-chain Bridge Monitoring
6. State Change Analysis
Analysis_Metrics = {
"Transaction_Velocity": {
"xxxx",
"xxxx",
"xxxx"
},
"Value_Distribution": {
"xxxx",
"value_patterns",
"outlier_detection"
},
"Network_Metrics": {
"degree_centrality",
"betweenness_centrality",
"clustering_coefficient"
}
}
Models = {
"Transaction_Classification": {
"algorithm": "XGBoost",
"features": [
"transaction_value",
"gas_used",
"contract_interaction",
"temporal_features",
"network_metrics"
],
"training_data": "10M+ labeled transactions"
},
"Address_Profiling": {
"algorithm": "Random Forest",
"features": [
"transaction_patterns",
"interaction_types",
"value_flows",
"temporal_behavior"
]
}
}
Clustering_Models = {
"DBSCAN": "Density-based transaction clustering",
"Isolation_Forest": "Anomaly detection",
"HDBSCAN": "Hierarchical density clustering",
"K-means": "Behavior pattern clustering"
}
Processing_Pipeline = {
"Ingestion_Rate": "100,000+ TPS",
"Analysis_Layers": {
"L1": "Basic pattern matching",
"L2": "Statistical analysis",
"L3": "ML model inference",
"L4": "Graph analysis"
},
"Response_Time": "<500ms"
}
Pattern_Engine = {
"Graph_Analysis": {
"algorithm": "Neo4j Graph Algorithms",
"metrics": [
"path_analysis",
"community_detection",
"centrality_measures",
"cycle_detection"
]
},
"Temporal_Analysis": {
"time_series_decomposition",
"seasonal_pattern_detection",
"burst_analysis",
"frequency_analysis"
}
}
Behavior_Metrics = {
"Transaction_Patterns": {
"frequency_analysis": "15-minute intervals",
"value_distribution": "Statistical moments",
"interaction_types": "Contract calls",
"gas_usage_patterns": "Consumption analysis"
},
"Network_Behavior": {
"connection_patterns": "Graph metrics",
"interaction_frequency": "Temporal analysis",
"value_flow_patterns": "Flow analysis"
}
}
Pattern_Evolution = {
"Learning_System": {
"feedback_loop": "Continuous learning",
"pattern_updates": "Real-time",
"adaptation_rate": "Every 1000 blocks"
},
"Pattern_Database": {
"storage": "Time-series DB",
"indexing": "Multi-dimensional",
"query_optimization": "Pattern-based"
}
}
Performance_Stats = {
"Processing_Capacity": "1M+ tx/hour",
"Pattern_Detection": {
"accuracy": "99.7%",
"false_positive": "0.3%",
"recall": "98.5%",
"precision": "99.1%"
},
"Response_Times": {
"simple_patterns": "<100ms",
"complex_analysis": "<2s",
"full_trace": "<5s"
}
}
Accuracy_Metrics = {
"Pattern_Recognition": {
"known_patterns": "99.9%",
"new_variants": "95%",
"zero_day": "85%"
},
"False_Positives": {
"rate": "0.1%",
"reduction_system": "AI-based",
"human_verification": "Required >$1M"
}
}
Architecture = {
"Processing_Nodes": "1000+",
"Data_Sharding": "Chain-based",
"Load_Balancing": "Dynamic",
"Redundancy": "3x",
"Failover": "Automatic"
}
Optimization = {
"Caching_Layers": {
"L1": "Pattern cache",
"L2": "Transaction cache",
"L3": "State cache"
},
"Query_Optimization": {
"indexed_patterns": "Pre-computed",
"dynamic_routing": "Load-based",
"parallel_processing": "GPU acceleration"
}
}
This system processes millions of transactions and patterns through multiple layers of analysis, combining traditional pattern matching with advanced machine learning and statistical analysis. The system continuously evolves and learns from new patterns while maintaining high accuracy and low false positive rates.