Bot Detection

Bot detection identifies traffic generated by automated scripts or programs rather than real users. Bots range from simple scripts replaying stolen credentials to sophisticated frameworks that mimic human browsing behavior, including mouse movements and realistic timing patterns.

Detection techniques operate at multiple layers. Network-level signals include IP reputation, request rate, and geographic anomalies. Browser-level signals examine JavaScript execution, canvas rendering, and the presence of automation frameworks. Behavioral signals analyze interaction patterns such as typing speed, navigation paths, and the timing between actions. Challenge-based approaches present puzzles that are trivial for humans but costly for automation.

The OWASP Automated Threats project catalogs common attack patterns including credential stuffing, account creation fraud, carding, and scraping, providing a shared vocabulary for classification.

For CIAM, bot detection protects the login, registration, and password-reset flows that are the most targeted customer-facing entry points, reducing fraud while preserving a smooth experience for legitimate users.

Sources

• OWASP Automated Threats to Web Applications: https://owasp.org/www-project-automated-threats-to-web-applications/