Invention Grant
- Patent Title: Predicting false positives from vulnerability scanners using data analytics and machine learning
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Application No.: US16700633Application Date: 2019-12-02
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Publication No.: US11381590B2Publication Date: 2022-07-05
- Inventor: Saeed A. Alsaeed , Irslan Siddiq
- Applicant: Saudi Arabian Oil Company
- Applicant Address: SA Dhahran
- Assignee: Saudi Arabian Oil Company
- Current Assignee: Saudi Arabian Oil Company
- Current Assignee Address: SA Dhahran
- Agency: Leason Ellis LLP
- Main IPC: H04L9/40
- IPC: H04L9/40 ; G06N20/00 ; G06N5/04

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.
Public/Granted literature
- US20210168165A1 PREDICTING FALSE POSITIVES FROM VULNERABILITY SCANNERS USING DATA ANALYTICS AND MACHINE LEARNING Public/Granted day:2021-06-03
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