Malware Detection Using Deep Learning


Malware Detection Using Deep Learning

Shreya Shah, Sneh Gaikwad, Kalpesh Tated and Santosh Tamboli

Shreya Shah, Sneh Gaikwad, Kalpesh Tated and Santosh Tamboli "Malware Detection Using Deep Learning" Published in International Journal of Trend in Research and Development (IJTRD), ISSN: 2394-9333, Volume-8 | Issue-3 , June 2021, URL: http://www.ijtrd.com/papers/IJTRD22606.pdf

In this study, we have used the Image Similarity technique to detect the unknown or new type of malware using CNN approach. CNN was investigated and tested with three types of datasets i.e. one from Vision Research Lab, which contains 9458 gray-scale images that have been extracted from the same number of malware samples that come from 25 different malware families, and second was benign dataset which contained 3000 different kinds of benign software. Benign dataset and dataset vision research lab were initially executable files which were converted in to binary code and then converted in to image files.

Malware Detection


Volume-8 | Issue-3 , June 2021

2394-9333

IJTRD22606
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