Unsupervised and supervised machine learning approaches to detecting bots and other types of browsers

Abstract

Unsupervised or supervised machine learning ('ML') techniques discussed herein can be used to classify browsers as one or more types of browser or within one or more browser groups. For example, a computer system configured to improve security of server computers interacting with client computers through an intermediary computer, and comprising: a memory comprising processor logic; one or more processors coupled to the memory, wherein the one or more processors execute the processor logic, which causes the one or more processors to: receive a first plurality of requests from a first plurality of browsers; generate a first plurality of request-feature vectors from the first plurality of requests; generate a plurality of browser groups based on the first plurality of request-feature vectors; receive a first new request from a first client computer; generate a first new request-feature vector based on the first new request; determine that the first new request-feature vector belongs to a first browser group among the plurality of browser groups; determine that the first browser group is associated with a first rule, and in response, respond to the first new request according to the first rule.