Deep learning for malicious image file detection

Patent metadata

Field	Value
Publication number	US-12452297-B2
Application number	US-202318199258-A
Country	US
Kind code	B2
Filing date	May 18, 2023
Priority date	May 18, 2023
Publication date	Oct 21, 2025
Grant date	Oct 21, 2025

Abstract

Techniques for using deep learning to identify malicious image files are disclosed. A plurality of sections of a first image are received. The received sections are used to determine a likelihood that the first image is malicious. The determination is made, at least in part, using a model trained using a set of sections extracted from a set of sample images. A verdict is provided for the first image.

First claim

What is claimed is:

1. A system, comprising:
a processor configured to:
receive a plurality of structural sections of a first image file, wherein the plurality of structural sections corresponds to structural elements associated with a file format of the first image file;
use the received plurality of structural sections to determine a likelihood that the first image file contains potentially malicious content in at least one of the structural sections, at least in part by using a model trained using a sample set comprising a plurality of previously processed image files, wherein a given image file included in the plurality of previously processed image files was processed at least in part by having a set of sections extracted by an image parser, and wherein the image parser is configured to extract both normal sections and abnormal sections from the given image file, wherein a given abnormal section is one that corresponds to a commonly exploited section; and
provide as output a verdict for the first image file; and

a memory coupled to the processor and configured to provide the processor with instructions.

2. The system of claim 1 , wherein the processor is further configured to parse the first image file to obtain the plurality of sections.

3. The system of claim 1 , wherein the plurality of sections includes a reserved section.

4. The system of claim 3 , wherein the reserved section includes data appearing after an end marker of the first image file.

5. The system of claim 3 , wherein the reserved section includes a malformatted chunk.

6. The system of claim 3 , wherein the reserved section includes a private chunk.

7. The system of claim 3 , wherein the processor is further configured to assign raw bytes to the reserved section.

8. The system of claim 1 , wherein the plurality of sections includes at least one of: a PLTE chunk, an IDAT chunk, or a tEXt chunk.

9. The system of claim 1 , wherein the plurality of sections includes at least one of: an APP0 segment, an APP1 segment, or a COM segment.

10. The system of claim 1 , wherein the model is a hierarchical convolutional neural network model.

11. The system of claim 1 , wherein the processor is configured to receive the first image file over a network and from a data appliance.

12. The system of claim 1 , wherein the processor is further configured to train the model.

13. A method, comprising:
receiving a plurality of structural sections of a first image file, wherein the plurality of structural sections correspond to structural elements associated with a file format of the first image file;
using the received plurality of structural sections to determine a likelihood that the first image file contains potentially malicious content in at least one of the structural sections, at least in part by using a model trained using a sample set comprising a plurality of previously processed image files, wherein a given image file included in the plurality of previously processed image files was processed at least in part by having a set of sections extracted by an image parser, and wherein the image parser is configured to extract both normal sections and abnormal sections from the given image file, wherein a given abnormal section is one that corresponds to a commonly exploited section; and
providing as output a verdict for the first image file.

14. A computer program product embodied in a non-transitory computer readable medium and comprising computer instructions for:
receiving a plurality of structural sections of a first image file, wherein the plurality of structural sections correspond to structural elements associated with a file format of the first image file;
using the received plurality of structural sections to determine a likelihood that the first image file contains potentially malicious content in at least one of the structural sections, at least in part by using a model trained using a sample set comprising a plurality of previously processed image files, wherein a given image file included in the plurality of previously processed image files was processed at least in part by having a set of sections extracted by an image parser, and wherein the image parser is configured to extract both normal sections and abnormal sections from the given image file, wherein a given abnormal section is one that corresponds to a commonly exploited section; and
providing as output a verdict for the first image file.

15. The method of claim 13 , further comprising parsing the first image file to obtain the plurality of sections.

16. The method of claim 13 , wherein the plurality of sections includes a reserved section.

17. The method of claim 16 , wherein the reserved section includes data appearing after an end marker of the first image file.

18. The method of claim 16 , wherein the reserved section includes a malformatted chunk.

19. The method of claim 16 , wherein the reserved section includes a private chunk.

20. The method of claim 16 , further comprising assigning raw bytes to the reserved section.

21. The method of claim 13 , wherein the plurality of sections includes at least one of:
a PLTE chunk, an IDAT chunk, or a tEXt chunk.

22. The method of claim 13 , wherein the plurality of sections includes at least one of:
an APP0 segment, an APP1 segment, or a COM segment.

23. The method of claim 13 , wherein the model is a hierarchical convolutional neural network model.

24. The method of claim 13 , wherein the processor is first image file is received over a network and from a data appliance.

25. The method of claim 13 , further comprising training the model.

Assignees

• Palo Alto Networks Inc

Inventors

• Du Min
• Sui Yijie
• Hewlett Ii William Redington
• Hu Wenjun

Classifications

• G06N3/045

  Combinations of networks · CPC title

• H04L63/20

  for managing network security; network security policies in general (filtering policies H04L63/0227) · CPC title

• H04L63/1416

  Event detection, e.g. attack signature detection · CPC title

• G06N3/08

  Learning methods · CPC title

• H04L63/145 Primary

  the attack involving the propagation of malware through the network, e.g. viruses, trojans or worms · CPC title