Malware Image Classification Using Deep Learning InceptionResNet-V2 and VGG-16 Method

Malware is intentionally designed to damage computers, servers, clients or computer networks. Malware is a general term used to describe any program designed to harm a computer or server. The goal is to commit a crime, such as gaining unauthorized access to a particular system, so as to compromise u...

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Bibliographic Details
Published in:	JOIN (Jurnal Online Informatika (Online) Vol. 8; no. 1; pp. 61 - 71
Main Authors:	Didih Rizki Chandranegara, Jafar Shodiq Djawas, Faiq Azmi Nurfaizi, Zamah Sari
Format:	Journal Article
Language:	English
Published:	Department of Informatics, UIN Sunan Gunung Djati Bandung 28-06-2023
Subjects:	classification convolutional neural network inceptionresnet-v2 malware vgg-16
Online Access:	Get full text
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Summary:	Malware is intentionally designed to damage computers, servers, clients or computer networks. Malware is a general term used to describe any program designed to harm a computer or server. The goal is to commit a crime, such as gaining unauthorized access to a particular system, so as to compromise user security. Most malware still uses the same code to produce another different form of malware variants. Therefore, the ability to classify similar malware variant characteristics into malware families is a good strategy to stop malware. The research is useful for classifying malware on malware samples presented as bytemap grayscale images. The malware classification research focused on 25 malware classes with a total of 9,029 images from the Malimg dataset. This research implements the VGG-16 and InceptionResNet-V2 architectures by running 2 different scenarios, scenario 1 uses the original dataset and the other scenario uses the undersampled dataset. After building the model, each scenario will get an evaluation form such as accuracy, precision, recall, and f1-score. The highest score was obtained in scenario 2 on the VGG-16 method with a score of 94.8% and the lowest in scenario 2 on the InceptionResNet-V2 method with a score of 85.1%.
ISSN:	2528-1682 2527-9165
DOI:	10.15575/join.v8i1.1051