Deep learning to detect abusive sequences of user activity in online network

Abstract

In an example embodiment, a deep learning algorithm is introduced that operates directly on a raw sequence of user activity in an online network. This allows the system to scalably leverage more of the available signal hidden in the data and stop adversarial attacks more efficiently than other machine-learned models. More particularly, each specific request path is translated into a standardized token that indicates the type of the request (e.g., profile view, search, login, etc.). This eliminates the need for human curation of features. Then, the standardized request paths are standardized to integers based on the frequency of that request path across all users. This allows information about how common a given type of request is to be provided to the machine-learned model. The integer array is the activity sequence that is fed into the deep learning algorithm.