Malware classification through image processing with a convolutional neural network

David Marin; Ulises Orozco-Rosas; Kenia Picos

doi:10.1117/12.2633133

3 October 2022 Malware classification through image processing with a convolutional neural network

David Marin, Ulises Orozco-Rosas, Kenia Picos

Author Affiliations +

Proceedings Volume 12225, Optics and Photonics for Information Processing XVI; 122250F (2022) https://doi.org/10.1117/12.2633133
Event: SPIE Optical Engineering + Applications, 2022, San Diego, California, United States

Abstract

This paper presents the implementation of a convolutional neural network employing two different malware datasets. These datasets are converted to images, processed, and resized to 64x64. Through image processing, the convolutional neural network can accurately classify the types of malware families in the datasets. Experimental results to validate the analysis and implementation are provided; they were specifically made to show the proposal’s effectiveness and efficiency.

Conference Presentation

Citation Download Citation

David Marin, Ulises Orozco-Rosas, and Kenia Picos "Malware classification through image processing with a convolutional neural network", Proc. SPIE 12225, Optics and Photonics for Information Processing XVI, 122250F (3 October 2022); https://doi.org/10.1117/12.2633133

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE