Architecture of an Anti-Bot Protection
summary
Presents a concrete anti-bot system that combines layered signals — IP, TCP/IP fingerprint, TLS ClientHello, HTTP/2 headers, browser JS and GPU rendering, and behavioral mouse/interval patterns — into reputation scores and ML models (binary classifiers, CatBoost-style training) to block or challenge bots. Describes a proxy-based scraping stack called scrapoxy and operational tuning: mixing datacenter/ISP/residential/mobile proxies, headless browsers (Selenium/Puppeteer/Playwright), and tools for app inspection (Charles/Frida), and notes that a small header-order tweak can stop about 80% of naive scrapers.
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chapters (9)
- 0:00Introduction
- 3:12Turing test and bots
- 8:53Browser and OS fingerprinting
- 13:26Behavioral analysis and ML
- 17:39AntiBot architecture
- 19:23Bypass methodology
- 25:00Advanced protection bypass
- 30:13Deobfuscation and future
- 33:00Conclusion
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