Abstract: A method for prioritizing security events comprises receiving a security event that includes security event data having been generated by an endpoint agent based on a detected activity, wherein the security event data includes one or more features; applying a first computing model to the security event data to automatically determine which of the one or more features are one or more input features to a machine learning system; applying a second computing model to historical data related to the security event data to determine time pattern information of the security event data as an input to the machine learning system; combining the one or more input features from the first computing model and the input from the second computing model to generate a computed feature result; and generating an updated security level value of the security event from the computed feature result.

Abstract: Embodiments disclosed include methods and apparatus for detecting a reputation of infrastructure associated with potentially malicious content. In some embodiments, an apparatus includes a memory and a processor. The processor is configured to identify an Internet Protocol (IP) address associated with potentially malicious content and define each row of a matrix by applying a different subnet mask from a plurality of subnet masks to a binary representation of the IP address to define that row of the matrix. The processor is further configured to provide the matrix as an input to a machine learning model, and receive, from the machine learning model, a score associated with a maliciousness of the IP address.