A deep learning framework for malware classification

Document Type

Article

Source of Publication

International Journal of Digital Crime and Forensics

Publication Date

1-1-2020

Abstract

Copyright © 2020, IGI Global. 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.

ISSN

1941-6210

Publisher

IGI Global

Volume

12

Issue

1

First Page

90

Last Page

108

Disciplines

Computer Sciences

Keywords

Convolutional Neural Networks, Deep Learning, Framework, Malware Classification

Scopus ID

85074538720

Indexed in Scopus

yes

Open Access

yes

Open Access Type

Bronze: This publication is openly available on the publisher’s website but without an open license

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