Detecting robotic internet activity across domains utilizing one-class and domain adaptation machine-learning models

In a digital medium environment for monitoring Internet traffic, a system for detecting robotic activity comprising:

• at least one processor;
• and
• a non-transitory computer memory comprising instructions that, when executed by the at least one processor, cause the system to:
• identify network session data for a domain, the network session data comprising a plurality of network sessions;
• identify a first subset of network sessions comprising human activity;
• identify a second subset of network sessions comprising good robotic activity;
• generate, using the first subset of network sessions, a first classifier to output a probability that a network session corresponds to human activity given a set of features for the network session;
• generate, using the second subset of network sessions, a second classifier to output a probability that the network session corresponds to good robotic activity given the set of features for the network session;
• and
• generate, using the first classifier and the second classifier, an activity classifier model that determines whether network sessions correspond to human activity, good robotic activity, or bad robotic activity.

In a digital medium environment for monitoring Internet traffic, a computer-implemented method of detecting robotic activity comprising:

• identifying network session data for each domain of a plurality of domains, the network session data comprising a plurality of network sessions with a first subset of network sessions comprising human activity and a second subset of network sessions comprising good robotic activity;
• a step for generating, for each domain of the plurality of domains, an activity classifier model that determines whether network sessions at a domain of the plurality of domains correspond to human activity, good robotic activity, or bad robotic activity using a first classifier corresponding to human activity and a second classifier corresponding to good robotic activity;
• and
• a step for generating, using a plurality of activity classifier models for the plurality of domains, a domain-agnostic machine-learning model for determining whether network sessions correspond to human activity or robotic activity irrespective of a domain of the network sessions.