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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
computer science
Image-Based malware classification using ensemble of CNN architectures (IMCEC)
Computers and Security, Volume 92, Article 101748, Year 2020
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Description
Both researchers and malware authors have demonstrated that malware scanners are unfortunately limited and are easily evaded by simple obfuscation techniques. This paper proposes a novel ensemble convolutional neural networks (CNNs) based architecture for effective detection of both packed and unpacked malware. We have named this method Image-based Malware Classification using Ensemble of CNNs (IMCEC). Our main assumption is that based on their deeper architectures different CNNs provide different semantic representations of the image; therefore, a set of CNN architectures makes it possible to extract features with higher qualities than traditional methods. Experimental results show that IMCEC is particularly suitable for malware detection. It can achieve a high detection accuracy with low false alarm rates using malware raw-input. Result demonstrates more than 99% accuracy for unpacked malware and over 98% accuracy for packed malware. IMCEC is flexible, practical and efficient as it takes only 1.18 s on average to identify a new malware sample. © 2020 Elsevier Ltd
Authors & Co-Authors
Alazab, Mamoun
Australia, Darwin
Charles Darwin University
Safaei, Babak
Turkey, Famagusta
Eastern Mediterranean University
Statistics
Citations: 208
Authors: 2
Affiliations: 6
Identifiers
Doi:
10.1016/j.cose.2020.101748
ISSN:
01674048