User Behavior Analytics

Technical Definition

User Behavior Analytics (UBA) for bot detection analyzes interaction patterns to distinguish humans from automated systems. This includes tracking mouse movement trajectories, scroll patterns, typing rhythms, click timing, and session navigation flows. Humans exhibit natural variability and imperfect cursor control—slight hesitations, curved mouse paths, variable scroll speeds. Bots produce mechanically precise movements, consistent timing, and predictable navigation sequences. UBA systems create behavioral fingerprints from these patterns, scoring sessions for bot probability.

Business Use Case

Online ticketing sites use UBA to detect scalper bots that purchase tickets within milliseconds of release. Human buyers show natural mouse trajectory curves and hesitation before clicking “buy,” while bots move directly and click instantly. Banking UBA detects automated account takeover attempts by analyzing whether login-to-transfer navigation follows natural human patterns or suspiciously efficient bot workflows.

Pro-Tip

Evading UBA requires human-like noise injection into all interaction patterns. Introduce random micro-delays between actions, add slight cursor path curvature to direct movements, vary scroll speeds, and introduce occasional “mistakes” and corrections that humans naturally exhibit. The most effective approach is using recorded human sessions replayed through automation frameworks rather than algorithmic interaction generation.