Cyber-threat score generation using machine learning and reflecting quality of sources

Abstract

A cyber-security analysis method uses machine learning (ML) technology to classify cyber-threat indicators, for example, as malicious or benign, by generating a threat score. The method includes receiving, at a compute device, a data set including cyber-threat indicators and verdicts serving as votes from each source in the set of sources. Each of the votes is associated with one of the cyber-threat indicators. An ML model is trained based on at least one of agreements among the sets of votes, and disagreements among the sets of votes to produce a trained ML model. In response to receiving a new cyber-threat indicator, votes are identified for each source from a subset of the sources, to define a second set of votes. The cyber-threat score is generated for the new cyber-threat indicator based on the trained ML model and the second set of votes.