Ensemble Malware Classification System Using Deep Neural Networks

Author(s)

Barath Narayanan Narayanan (0000-0001-9648-5695), University of DaytonFollow
Venkata Salini Priyamvada Davuluru, University of DaytonFollow

Document Type

Article

Publication Date

4-27-2020

Publication Source

Electronics

Abstract

With the advancement of technology, there is a growing need of classifying malware programs that could potentially harm any computer system and/or smaller devices. In this research, an ensemble classification system comprising convolutional and recurrent neural networks is proposed to distinguish malware programs. Microsoft's Malware Classification Challenge (BIG 2015) dataset with nine distinct classes is utilized for this study. This dataset contains an assembly file and a compiled file for each malware program. Compiled files are visualized as images and are classified using Convolutional Neural Networks (CNNs). Assembly files consist of machine language opcodes that are distinguished among classes using Long Short-Term Memory (LSTM) networks after converting them into sequences. In addition, features are extracted from these architectures (CNNs and LSTM) and are classified using a support vector machine or logistic regression. An accuracy of 97.2% is achieved using LSTM network for distinguishing assembly files, 99.4% using CNN architecture for classifying compiled files and an overall accuracy of 99.8% using the proposed ensemble approach thereby setting a new benchmark. An independent and automated classification system for assembly and/or compiled files provides the luxury to anti-malware industry experts to choose the type of system depending on their available computational resources.

ISBN/ISSN

2079-9292

Document Version

Published Version

Comments

This open-access article is provided for download in compliance with the publisher’s policy on self-archiving. To view the version of record, use the DOI: https://doi.org/10.3390/electronics9050721

Publisher

MDPI

Volume

9

Peer Reviewed

yes

Issue

5

eCommons Citation

Narayanan, Barath Narayanan and Davuluru, Venkata Salini Priyamvada, "Ensemble Malware Classification System Using Deep Neural Networks" (2020). Electrical and Computer Engineering Faculty Publications. 470.
https://ecommons.udayton.edu/ece_fac_pub/470