Biometric behavioral verification

Reblaze does not limit its traffic analysis to the user environment and client session. It also performs extensive, continual analysis of the user’s behavior.

Every HTTP request that Reblaze receives is anonymized and then analyzed according to numerous factors, including (partial list):

  • Device and software data (the user’s hardware, its screen resolution and orientation, the software used, battery level, stack trace, fronts and extensions, emulator detection, window size, hidden iframes, etc.)

  • User interface and events (mouse/pointer movements, clicks, taps, zooms, scrolls, keystrokes, speed of entry, etc.)

  • Session data (requests sent, IPs used, timing, frequency, etc.)

  • Consumption analytics (pages viewed, time spent, resources requested, etc.)

  • Application-specific events (and other results of user actions.)

Reblaze understands the patterns, typical values, and common relationships of these metrics for legitimate users of each protected application and API. The amount of data that Reblaze processes (over four billion requests per day) is far beyond the capability of human analysts. Therefore, cloud-based compute resources are used, applying Machine Learning in order to recognize patterns that analysts could not have identified on their own, or for which they might not have thought to look.

Reblaze performs this analysis to an extremely granular level: not only per app, but even down to individual pages, screens, and so on. This reveals patterns of behavior unique to that particular context.

Reblaze continually analyzes the activities and behaviors of every requestor in every session. By definition, every hostile user (whether human or bot), must, at some point, deviate from the behavior of a legitimate user. As soon as this occurs, Reblaze blocks that requestor.

Using this approach, Reblaze’s bot detection accuracy is not only high, it is also robust and resistant to reverse-engineering by threat actors. Behavioral profiles are constructed based on private analytics data, and threat actors have no realistic way of obtaining this information.

Biometric behavioral verification is part of the passive challenge process. To enable behavioral analysis, passive challenges must be enabled.

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