Malware Classification with Deep Convolutional Neural Networks

Document Type

Conference Proceeding

Source of Publication

2018 9th IFIP International Conference on New Technologies, Mobility and Security, NTMS 2018 - Proceedings

Publication Date

3-29-2018

Abstract

© 2018 IEEE. In this paper, we propose a deep learning framework for malware classification. There has been a huge increase in the volume of malware in recent years which poses a serious security threat 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 so that their behavior can be analyzed. Machine learning approaches are becoming popular for classifying malware, however, most of the existing machine learning methods for malware classification use shallow learning algorithms (e.g. SVM). Recently, Convolutional Neural Networks (CNN), a deep learning approach, have shown superior performance compared to traditional learning algorithms, especially in tasks such as image classification. Motivated by this success, we propose a CNN-based architecture to classify malware samples. We convert malware binaries to grayscale images and subsequently train a CNN for classification. Experiments on two challenging malware classification datasets, Malimg and Microsoft malware, demonstrate that our method achieves better than the state-of-the-art performance. The proposed method achieves 98.52% and 99.97% accuracy on the Malimg and Microsoft datasets respectively.

ISBN

9781538636626

Publisher

Institute of Electrical and Electronics Engineers Inc.

Volume

2018-January

First Page

1

Last Page

5

Disciplines

Computer Sciences | Education

Keywords

convolutional neural networks, deep learning, Malware classification

Scopus ID

85051034525

Indexed in Scopus

yes

Open Access

no

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