Visual Profiling and Automated Classification of Malware Samples using Deep Learning

DOI

10.2991/978-94-6463-314-6_25How to use a DOI?

Keywords

Malware; Visualization; Detection; Classification; Deep Learning

Abstract

Information security is facing a significant issue due to the proliferation of malware programs. Malware analysis refers to the process of interpreting malicious software to determine its functionality and intent and assist in detection. Conventional methods, which rely on both static and dynamic analyses for malware identification and categorization, often strive to keep up with the ever-rising evolution of malware. Therefore, our proposal presents a thorough deep learning powered malware analysis system that is divided into three essential modules: data processing, feature extraction, detection, and classification. The data processing module handles converting binary data into grayscale photos specifically, includes an import feature, and skillfully extracts essential virus information. This module makes effective use of these extracted attributes to identify potentially suspicious samples and classify malware cases. The Detection and classification module, which completed the architecture, uses deep learning algorithms to identify malware and classify into respected families, resulting in a strong and proactive approach to cybersecurity. This paper contributes to the realm of enhanced cybersecurity by providing a method that not only enhances accuracy but also has the potential to adapt to emerging malware threats.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - P. Subhash
AU  - Y. Sri Varsha
AU  - K. Saketh Reddy
AU  - B. Akshaya
AU  - S. Kalyani
PY  - 2023
DA  - 2023/12/21
TI  - Visual Profiling and Automated Classification of Malware Samples using Deep Learning
BT  - Proceedings of the International e-Conference on Advances in Computer Engineering and Communication Systems (ICACECS 2023)
PB  - Atlantis Press
SP  - 249
EP  - 257
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-314-6_25
DO  - 10.2991/978-94-6463-314-6_25
ID  - Subhash2023
ER  -