Machine learning has transformed bot detection. Here's how we use ML to identify sophisticated bots.
The Challenge
Rule-based detection can catch obvious bots, but sophisticated attackers adapt. They study detection methods and craft requests that pass static checks.
Our Approach
We train models on billions of labeled requests. The models learn patterns that distinguish human and bot traffic—patterns too subtle for humans to codify as rules.
Features
Our models consider hundreds of features:
- Request timing and sequencing
- Browser fingerprint consistency
- Navigation patterns
- Network characteristics
Real-Time Inference
Models run at the edge using optimized inference engines. Predictions happen in microseconds.
Continuous Learning
The threat landscape evolves constantly. Our models retrain regularly on new data, adapting to emerging attack patterns automatically.