Document Type
Conference Paper
Version Deposited
Published Version
Publication Date
3-2022
Publication Title
Proceedings of the 17th International Conference on Cyber Warfare and Security
Conference Name
International Conference on Cyber Warfare and Security
DOI
10.34190/iccws.17.1.59
Abstract
The persistent shortage of cybersecurity professionals combined with enterprise networks tasked with processing more data than ever before has led many cybersecurity experts to consider automating some of the most common and time-consuming security tasks using machine learning. One of these cybersecurity tasks where machine learning may prove advantageous is malware analysis and classification. To evade traditional detection techniques, malware developers are creating more complex malware. This is achieved through more advanced methods of code obfuscation and conducting more sophisticated attacks. This can make the manual process of analyzing malware an infinitely more complex task. Furthermore, the proliferation of malicious files and new malware signatures increases year by year. As of March 2020, the total number of new malware detections worldwide amounted to 677.66 million programs. In 2020, there was a 35.4% increase in new malware variants over the previous year. This paper examines the viability of classifying malware binaries represented as fixed-size grayscale using convolutional neural networks. Several Convolutional Neural Network (CNN) architectures are evaluated on multiple performance metrics to analyze their effectiveness at solving this classification problem.
Recommended Citation
J. Kiger, S.-S. Ho, and V. Heydari, “Malware Binary Image Classification Using Convolutional Neural Networks,” International Conference on Cyber Warfare and Security, vol. 17, no. 1. Academic Conferences International Ltd, pp. 469–478, Mar. 02, 2022. doi: 10.34190/iccws.17.1.59.
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.