Bot detection is the process of separating automated requests from human-driven traffic. Security systems use signals such as request patterns, browser behavior, device fingerprints, JavaScript challenges, IP reputation, and timing anomalies to estimate whether a visitor is a real user or a script. The goal is to stop scraping, credential stuffing, carding, fraud, and abuse without blocking legitimate customers.
In practice, bot detection is rarely perfect because attackers can mimic browsers, rotate proxies, and adjust behavior to evade simple checks. That is why modern defenses use layered controls: rate limiting, risk scoring, token-based proof, and fallback challenges for suspicious sessions. Browser-native tokens and similar mechanisms can improve confidence by giving sites a stronger legitimacy signal than headers alone, but they still need logging, monitoring, and abuse response to handle replay, impersonation, and other adaptation by automation.