Bot detection has evolved significantly over the past decade. What started as simple IP blocklists has grown into a sophisticated discipline combining machine learning, behavioral analysis, and real-time decision making.
The Evolution of Bots
Early bots were simple scripts that made obvious HTTP requests. They were easy to detect based on missing headers, unusual request patterns, or known bad IP addresses.
Modern bots are different. They:
- Use real browsers through automation frameworks
- Rotate through residential IP addresses
- Mimic human behavior patterns
- Solve CAPTCHAs using ML or human farms
Detection Techniques
Fingerprinting
Device fingerprinting collects information about the client's browser, operating system, and hardware. Inconsistencies between these signals can indicate automation.
Behavioral Analysis
Real humans have distinct patterns—mouse movements are curved, typing has rhythm, and interactions follow natural timing. Bots often fail to replicate these patterns convincingly.
Machine Learning
ML models can identify patterns that humans can't see. By training on billions of requests, we can detect subtle anomalies that indicate bot activity.
The Future
As bots get more sophisticated, detection must evolve too. We're investing heavily in research to stay ahead of emerging threats.