Malware Detection Using Image Visualization and Deep Learning

Saksham Tyagi

Despite the relentless efforts of cybersecurity research to protect against malware threats, malware developers discover new ways to avoid these defense techniques.Usual machine learning approaches that train a classifier based on handcrafted features are not sufficiently potent against the new evasive techniques and require more efforts due to feature-engineering. We propose a visualization-based method, where malware binaries are depicted as images to successfully distinguish between malware files and clean files using a deep learning model. Extensive experiments performed on Malimg dataset shows the accuracy to improve up to 96.97 percent.

Published Paper   E-Certificate

"Malware Detection Using Image Visualization and Deep Learning", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.7, Issue 11, page no.164 - 166, November-2022, Available :https://ijsdr.org/papers/IJSDR2211027.pdf

Volume 7 Issue 11, November-2022

Pages : 164 - 166

Paper Reg. ID: IJSDR_202474

Published Paper Id: IJSDR2211027

Downloads: 000347193

Research Area: Computer Science & Technology 

Country: Hasanpur, Uttar Pradesh, India

Published Paper PDF: https://ijsdr.org/papers/IJSDR2211027

Published Paper URL: https://ijsdr.org/viewpaperforall?paper=IJSDR2211027

DOI: http://doi.one/10.1729/Journal.32139

ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJSDR(IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition