Using threat intelligence to manage software fixes

Patent metadata

Field	Value
Publication number	US-12462042-B2
Application number	US-202318533849-A
Country	US
Kind code	B2
Filing date	Dec 8, 2023
Priority date	Dec 8, 2023
Publication date	Nov 4, 2025
Grant date	Nov 4, 2025

Abstract

A defect in the code of a software program is identified. An initial ranking for the defect in the code of the software program is determined. One or more network websites are crawled to identify information associated with the defect in the code of the software program. The information associated with the defect in the defect in the code of the software program is analyzed. In response to analyzing the information associated with the defect in the code of the software program, a second ranking is created for the defect in the code of the software program. The defects in the code of the software program and the second ranking are generated for display in a graphical user interface. By prioritizing which defects are more critical, the quality of the released software improved. In addition, the released software is more secure because critical defects have been removed.

First claim

What is claimed is:

1 . A system comprising:
a microprocessor; and a computer readable medium, coupled with the microprocessor and comprising microprocessor readable and executable instructions that, when executed by the microprocessor, cause the microprocessor to: get a defect in code of a software program; get an initial ranking for the defect in the code of the software program; crawl one or more network websites to identify information associated with the defect in the code of the software program, wherein the information associated with the defect in the code of the software program comprises first source code to attack the defect; analyze the information associated with the defect in the code of the software program; in response to analyzing the information associated with the defect in the code of the software program, create a second ranking for the defect in the code of the software program; generate for display, in a graphical user interface, the defect in the code of the software program and the second ranking: retrieve the first source code to attack the defect; analyze the first source code to attack the defect using a machine learning algorithm; and generate, by the machine learning algorithm, second source code to mitigate the first source code to attack the defect.

2 . The system of claim 1 , wherein the microprocessor readable and executable instructions further cause the microprocessor to at least one of:
fix the defect in the code of the software program; and generate, for display in a graphical user interface, the second source code to mitigate the first source code to attack the defect.

3 . The system of claim 1 , wherein the machine learning algorithm is trained using source code used to attack a plurality of defects and the corresponding fixes.

4 . The system of claim 1 , wherein the microprocessor readable and executable instructions further cause the microprocessor to:
identify a link to a second network website in the information associated with the defect in the code of the software program, wherein the second network website is not one of the one or more network websites; and in response to identifying the link to the second network website in the information associated with the defect in the code of the software program, crawl the second network website to identify additional information associated with the defect in the code of the software program.

5 . The system of claim 4 , wherein the microprocessor readable and executable instructions further cause the microprocessor to:
determine, if the additional information associated with the defect in the code of the software program meets defect criteria; and in response to determining that the additional information associated with the defect in the code of the software program meets the defect criteria, save an address of the second network website.

6 . The system of claim 1 , wherein the information associated with the defect in the code of the software program comprises one or more of: a notorious user, an expert user, a fake poster, a semi-legitimate poster, a commentor on a post, users looking at the post, source code to attack the defect, an increase of chatter on a dark web about the defect, a decrease of the chatter on the dark web about the defect, a number of companies impacted by the defect, a number of articles published about the defect, a federal government fix date for the defect, a public posting of private information about the defect, a potential financial impact of the defect, and a number of companies reporting the defect.

7 . The system of claim 1 , wherein analyzing the information associated with the defect in the code of the software program is accomplished by a machine learning algorithm and wherein the microprocessor readable and executable instructions further cause the microprocessor to:
receive, from a graphical user interface, feedback from a user about the second ranking; and in response to receiving the feedback from the user about the second ranking, providing the information about the second ranking to retrain the machine learning algorithm.

8 . A method comprising:
identifying, by a microprocessor, a defect in code of a software program; get, by the microprocessor, an initial ranking for the defect in the code of the software program; crawling, by the microprocessor, one or more network websites to identify information associated with the defect in the code of the software program, wherein the information associated with the defect in the code of the software program comprises first source code to attack the defect; analyzing, by the microprocessor, the information associated with the defect in the code of the software program; in response to analyzing the information associated with the defect in the code of the software program, creating, by the microprocessor, a second ranking for the defect in the code of the software program; generating for display, by the microprocessor, in a graphical user interface, the defect in the code of the software program and the second ranking; retrieving the first source code to attack the defect; analyzing the first source code to attack the defect using a machine learning algorithm; and generating, by the machine learning algorithm, second source code to mitigate the first source code to attack the defect.

9 . The method of claim 8 , further comprising at least one of:
fixing the defect in the code of the software program; and generating, for display in a graphical user interface, the second source code to mitigate the first source code to attack the defect.

10 . The method of claim 8 , wherein the machine learning algorithm is trained using source code used to attack a plurality of defects and the corresponding fixes.

11 . The method of claim 8 , further comprising:
identifying a link to a second network website in the information associated with the defect in the code of the software program, wherein the second network website is not one of the one or more network websites; and in response to identifying the link to the second network website in the information associated with the defect in the code of the software program, crawling the second network website to identify additional information associated with the defect in the code of the software program.

12 . The method of claim 11 , further comprising:
determining, if the additional information associated with the defect in the code of the software program meets defect criteria; and in response to determining that the additional information associated with the defect in the code of the software program meets the defect criteria, saving an address of the second network website.

13 . The method of claim 8 , wherein the information associated with the defect in the code of the software program comprises one or more of: a notorious user, an expert user, a fake poster, a semi-legitimate poster, a commentor on a post, users looking at the post, source code to attack the defect, an increase of chatter on a dark web about the defect, a decrease of the chatter on the dark web about the defect, a number of companies impacted by the defect, a number of articles published about the defect, a federal government fix date for the defect, a public posting of private information about the defect, a potential financial impact of the defect, and a number of companies reporting the defect.

14 . The method of claim 8 , wherein analyzing the information associated with the defect in the code of the software program is accomplished by a machine learning algorithm and further comprising:

Assignees

• Angelo Michael F
• Hoole Alexander Michael
• Grover Douglas Max
• Micro Focus Llc

Inventors

• Angelo Michael F
• Hoole Alexander Michael
• Grover Douglas Max

Classifications

• G06F16/951

  Indexing; Web crawling techniques · CPC title

• G06F21/577 Primary

  Assessing vulnerabilities and evaluating computer system security · CPC title