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A Deep Learning Framework for Malware Classification

A Deep Learning Framework for Malware Classification

Mahmoud Kalash, Mrigank Rochan, Noman Mohammed, Neil Bruce, Yang Wang, Farkhund Iqbal
Copyright: © 2020 |Volume: 12 |Issue: 1 |Pages: 19
ISSN: 1941-6210|EISSN: 1941-6229|EISBN13: 9781799805793|DOI: 10.4018/IJDCF.2020010105
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MLA

Kalash, Mahmoud, et al. "A Deep Learning Framework for Malware Classification." IJDCF vol.12, no.1 2020: pp.90-108. http://doi.org/10.4018/IJDCF.2020010105

APA

Kalash, M., Rochan, M., Mohammed, N., Bruce, N., Wang, Y., & Iqbal, F. (2020). A Deep Learning Framework for Malware Classification. International Journal of Digital Crime and Forensics (IJDCF), 12(1), 90-108. http://doi.org/10.4018/IJDCF.2020010105

Chicago

Kalash, Mahmoud, et al. "A Deep Learning Framework for Malware Classification," International Journal of Digital Crime and Forensics (IJDCF) 12, no.1: 90-108. http://doi.org/10.4018/IJDCF.2020010105

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Abstract

In this article, the authors propose a deep learning framework for malware classification. There has been a huge increase in the volume of malware in recent years which poses serious security threats to financial institutions, businesses, and individuals. In order to combat the proliferation of malware, new strategies are essential to quickly identify and classify malware samples. Nowadays, machine learning approaches are becoming popular for malware classification. However, most of these approaches are based on shallow learning algorithms (e.g. SVM). Recently, convolutional neural networks (CNNs), a deep learning approach, have shown superior performance compared to traditional learning algorithms, especially in tasks such as image classification. Inspired by this, the authors propose a CNN-based architecture to classify malware samples. They convert malware binaries to grayscale images and subsequently train a CNN for classification. Experiments on two challenging malware classification datasets, namely Malimg and Microsoft, demonstrate that their method outperforms competing state-of-the-art algorithms.