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🔍 Detection Methods: Spot MEV Bots

Learn how to detect front-running in the mempool

Understand MEV and transaction ordering exploits

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Detection Methods

Detecting front-running attacks requires monitoring multiple signals across the transaction lifecycle. From mempool visibility to profit extraction patterns, each detection method reveals different attack characteristics.

Combining multiple detection techniques provides comprehensive coverage. No single method catches all attacks, but layered monitoring creates a robust defense system.

Interactive: Detection Method Explorer

Explore different front-running detection techniques, their indicators, and tools used by security researchers.

Mempool Monitoring

Scan pending transactions for suspicious patterns

Effectiveness
85%
Implementation Difficulty
Medium

🔍 Key Indicators:

•Multiple transactions targeting same pool
•Transactions with gas prices 2x+ above average
•Same "from" address in quick succession
•Transaction ordering patterns (front-run, victim, back-run)

🛠️ Tools & Resources:

BlocknativeMEV-InspectFlashbots Dashboard

🎯 Detection Best Practices

1.
Monitor Multiple Signals
Combine mempool monitoring, gas analysis, and profit tracking for comprehensive detection
2.
Use Historical Data
Analyze past attacks to identify attacker addresses and patterns
3.
Implement Real-Time Alerts
Set up automated notifications when suspicious patterns are detected
4.
Collaborate & Share Intelligence
Join MEV research communities to share attack patterns and mitigation strategies

False Positives

Legitimate high-value transactions can trigger alerts. Use multiple confirmation signals before flagging as attack.

Common causes: Arbitrage bots, liquidations, MEV searchers

Detection Lag

Some methods detect attacks post-execution. Real-time prevention requires predictive monitoring.

Solution: Combine pre-execution and post-execution analysis
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