US 12,273,374 B2
Malware detection using frequency domain-based image visualization and deep learning
Tajuddin Manhar Mohammed, Goleta, CA (US); Lakshmanan Nataraj, Chennai (IN); Bangalore S. Manjunath, Santa Barbara, CA (US); and Shivkumar Chandrasekaran, Santa Barbara, CA (US)
Assigned to Mayachitra, Inc., Santa Barbara, CA (US)
Filed by Mayachitra, Inc., Santa Barbara, CA (US)
Filed on Mar. 24, 2021, as Appl. No. 17/211,019.
Prior Publication US 2022/0311782 A1, Sep. 29, 2022
Int. Cl. H04L 9/40 (2022.01); G06F 18/24 (2023.01); H04L 41/16 (2022.01)
CPC H04L 63/145 (2013.01) [G06F 18/24 (2023.01); H04L 41/16 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method comprising:
accessing, using a hardware processor, a computer file comprising a plurality of bytes;
generating a first image of the computer file based on the plurality of bytes;
determining a frequency count of bigrams in the computer file;
computing a discrete cosine transform (DCT) of the frequency count of bi-grams;
generating a second image of the computer file based on the DCT of the frequency count of the bi-grams;
analyzing, by an image classification neural network, the first image and the second image, wherein the analyzing comprises:
determining a joint feature metric based on a first set of image features computed from the first image and a second set of image features computed from the second image; and
computing a joint feature score for the joint feature metric based on a matrix L2-norm of an error-analysis matrix for the first set of image features and the second set of image features; and
generating a classification of the computer file based on the analyzing the first image and the second image.