Bot Detection

Bot detection combines several classes of evidence rather than relying on any single tell. Behavioral analysis watches how a session moves through a page: mouse trajectories, scroll cadence, typing rhythm, and the timing between events. Humans produce noisy, variable input, while naive automation produces input that is too regular, too fast, or entirely absent.

Environmental analysis inspects the browser itself. Real browsers expose a large, internally consistent surface of APIs, plugins, fonts, and rendering behaviors. Automated environments frequently contradict themselves: a user agent claiming to be Chrome on Windows may fail to render a font that Windows always provides, expose an automation flag, or return timing that only a headless engine produces.

Reputation and network signals add a third layer. The origin IP address, its autonomous system, and its history of abusive traffic help weight the decision. A session that looks human but arrives through a hosting provider or a known proxy pool is treated with more suspicion than the same session from a residential connection.

Modern detectors fuse these layers into a probabilistic verdict rather than a binary rule. Because attackers adapt, detection engines are updated continuously and often score the confidence of the automation judgment instead of hard-blocking, so that ambiguous cases can be challenged or reviewed rather than silently dropped.