The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Sep. 05, 2023

Filed:

Dec. 22, 2020
Applicant:

Arkose Labs Holdings, Inc., San Francisco, CA (US);

Inventors:

Suresh Chari, Scarsdale, NY (US);

Ashish Kundu, Elmsford, NY (US);

Ian Michael Molloy, Westchester, NY (US);

Dimitrios Pendarakis, Westport, CT (US);

Assignee:

Arkose Labs Holdings, Inc., San Francisco, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 21/00 (2013.01); G06F 21/56 (2013.01); G06F 8/41 (2018.01); G06N 20/00 (2019.01); G06N 7/01 (2023.01);
U.S. Cl.
CPC ...
G06F 21/566 (2013.01); G06F 8/433 (2013.01); G06N 7/01 (2023.01); G06N 20/00 (2019.01); G06F 2221/033 (2013.01);
Abstract

Anomalous control and data flow paths in a program are determined by machine learning the program's normal control flow paths and data flow paths. A subset of those paths also may be determined to involve sensitive data and/or computation. Learning involves collecting events as the program executes, and associating those event with metadata related to the flows. This information is used to train the system about normal paths versus anomalous paths, and sensitive paths versus non-sensitive paths. Training leads to development of a baseline 'provenance' graph, which is evaluated to determine 'sensitive' control or data flows in the “normal” operation. This process is enhanced by analyzing log data collected during runtime execution of the program against a policy to assign confidence values to the control and data flows. Using these confidence values, anomalous edges and/or paths with respect to the policy are identified to generate a “program execution” provenance graph associated with the policy.


Find Patent Forward Citations

Loading…