Vision Based Malware Classification Using Deep Neural Network with Hybrid Data Augmentation

Md. Rahman, Md. Rahman, Md. Delwar Hossain, Hideya Ochiai, Youki Kadobayashi, Tanjim Sakib, Syed Ramadan

2024

Abstract

Preventing malware attacks is crucial, as they can lead to financial losses, privacy breaches, system downtime, and reputational damage. Various machine learning and deep learning techniques have been proposed for malware classification. However, to evade detection, files from the same family are often altered by malware developers using various approaches so that they appear to be separate files. They may even appear as previously unidentified, commonly referred to as zero-day threats. These attacks can compromise the robustness of deep learning models trained for malware classification. In this research, we developed six fine-tuned Deep Neural Network (DNN) classifiers for classifying malware represented as images. A hybrid data augmentation technique based on Deep Convolutional Generative Adversarial Network (DCGAN) and traditional image transformation methods has been proposed to train the classifiers, enabling them to better handle malware vari-ants. A subset of the publicly available Malimg dataset, comprising six-class and the whole dataset, were used in the experiment. Additionally, both datasets were expanded using the proposed augmentation technique to train the developed classifiers. Experimental results reveal that vision transformer-based classifiers, trained with the proposed data augmentation technique, achieve a maximum accuracy of 99.94% for six-class classification and 99.79% for 25-class classification.

Paper Citation

in Harvard Style

Rahman M., Delwar Hossain M., Ochiai H., Kadobayashi Y., Sakib T. and Ramadan S. (2024). Vision Based Malware Classification Using Deep Neural Network with Hybrid Data Augmentation. In Proceedings of the 10th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP; ISBN 978-989-758-683-5, SciTePress, pages 823-830. DOI: 10.5220/0012434400003648

in Bibtex Style

@conference{icissp24,
author={Md. Rahman and Md. Delwar Hossain and Hideya Ochiai and Youki Kadobayashi and Tanjim Sakib and Syed Ramadan},
title={Vision Based Malware Classification Using Deep Neural Network with Hybrid Data Augmentation},
booktitle={Proceedings of the 10th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP},
year={2024},
pages={823-830},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012434400003648},
isbn={978-989-758-683-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP
TI - Vision Based Malware Classification Using Deep Neural Network with Hybrid Data Augmentation
SN - 978-989-758-683-5
AU - Rahman M.
AU - Delwar Hossain M.
AU - Ochiai H.
AU - Kadobayashi Y.
AU - Sakib T.
AU - Ramadan S.
PY - 2024
SP - 823
EP - 830
DO - 10.5220/0012434400003648
PB - SciTePress