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:
Jun. 24, 2025

Filed:

Oct. 10, 2023
Applicant:

Sophos Limited, Abingdon, GB;

Inventors:

Joshua Daniel Saxe, Wichita, KS (US);

Ethan M. Rudd, Colorado Springs, CO (US);

Richard Harang, Alexandria, VA (US);

Assignee:

Sophos Limited, Abingdon, GB;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
H04L 29/06 (2006.01); G06F 18/214 (2023.01); G06F 18/24 (2023.01); G06F 21/56 (2013.01); G06N 3/04 (2023.01); G06N 3/045 (2023.01); G06N 5/01 (2023.01); G06N 20/20 (2019.01);
U.S. Cl.
CPC ...
G06F 21/56 (2013.01); G06F 18/214 (2023.01); G06F 18/24 (2023.01); G06F 21/562 (2013.01); G06F 21/563 (2013.01); G06N 3/04 (2013.01); G06N 3/045 (2023.01); G06N 5/01 (2023.01); G06N 20/20 (2019.01);
Abstract

An apparatus for detecting malicious files includes a memory and a processor communicatively coupled to the memory. The processor receives multiple potentially malicious files. A first potentially malicious file has a first file format, and a second potentially malicious file has a second file format different than the first file format. The processor extracts a first set of strings from the first potentially malicious file, and extracts a second set of strings from the second potentially malicious file. First and second feature vectors are defined based on lengths of each string from the associated set of strings. The processor provides the first feature vector as an input to a machine learning model to produce a maliciousness classification of the first potentially malicious file, and provides the second feature vector as an input to the machine learning model to produce a maliciousness classification of the second potentially malicious file.


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