Abstract: A distributed security system includes instances of a compute engine that can receive an event stream comprising event data associated with an occurrence of one or more events on one or more client computing devices and generate new event data based on the event data in the event stream. A predictions engine coupled in communication with the compute engine(s) receives the new event data and applies at least a portion of the received new event data to one or more machine learning models of the distributed security system based to the received new event data. The one or more machine learning models generate a prediction result that indicates whether the occurrence of the one or more events from which the new event data was generated represents one or more target behaviors, based on the applying of at least the portion of the received new event data to the one or more machine learning models according to the received new event data.

Abstract: The present invention relies on dynamic autoproxy configuration and more particularly to a method and system for selecting a Proxy/Socks Server according to some response time and availability criteria. It rests on a dynamic autoproxy mechanism using availability and response time probes. It relies on probes retrieving well known HTML pages through each Proxy/Socks Server, measuring associated response time, detecting Proxy/Socks failures and degradation of response time. It also uses a CGI (Common Gateway Interface) program for dynamically creating autoproxy code (in a preferred embodiment Javascript code) on an autoproxy URL (Universal resource locator) system for selecting said Proxy/Socks Server.