Predicting false positives from vulnerability scanners using data analytics and machine learning
Abstract:
A method and system for remediating a vulnerability in a first computing resource asset in a computer network, including receiving vulnerability scanning results data from each respective one of a plurality of diverse vulnerability scanners, storing the vulnerability scanning results data as a collection of vulnerability scanning results data, normalizing and extracting common features from the normalized data, vectorizing the common features to determine feature vectors, applying a false positive predictor model to predict a false positive, separating vulnerability scanning results data that corresponds to one or more predicted false positives from a remainder of the collection of vulnerability scanning result data, and sending the remainder of the collection of vulnerability scanning results data to a second computer resource asset.
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