EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
Hacker News·Aug 20·5 min read
Detecting scraper bots through scroll behaviour
TL;DR
A new method for detecting scraper bots leverages the bursty scrolling behavior of humans, distinguishing it from the linear patterns of bots. This approach shows promise in improving bot detection accuracy.
✦ Why It Matters
Implement burstiness analysis in your bot detection systems to improve accuracy against advanced scraping techniques.
Key Takeaways
- Analyze scroll patterns to differentiate between human and bot interactions.
- Consider integrating additional features like mouse movement for better accuracy.
- Utilize burstiness and memory metrics in your bot detection models.
- Test your models in real-world scenarios to validate effectiveness.
How It Works
The method leverages burstiness and memory metrics to analyze the timing of scroll events. Humans exhibit a bursty scrolling pattern, while bots tend to scroll linearly.
By calculating these metrics for different agents, the model can classify interactions based on their scrolling behavior.