Abstract: A system, a method, and a computer program for analyzing network security data from diverse data sources to predict and remediate a vulnerability at a node in a computer network, comprising receiving network security data from a plurality of data sources, extracting feature vectors from the received network security data, applying a machine learning model to the extracted feature vectors to generate a weighted vulnerability value, predicting a computing resource vulnerability at a node in the computer network based on the weighted vulnerability value, and transmitting the predicted computing resource vulnerability to a computing device which is configured to remediate the predicted computing resource vulnerability.

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.

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.

Abstract: A system, a method, and a computer program for analyzing network security data from diverse data sources to predict and remediate a vulnerability at a node in a computer network, comprising receiving network security data from a plurality of data sources, extracting feature vectors from the received network security data, applying a machine learning model to the extracted feature vectors to generate a weighted vulnerability value, predicting a computing resource vulnerability at a node in the computer network based on the weighted vulnerability value, and transmitting the predicted computing resource vulnerability to a computing device which is configured to remediate the predicted computing resource vulnerability.