Using 3D-VGG-16 and 3D-Resnet-18 deep learning models and FABEMD techniques in the detection of malware

Al-Khater, Wadha; Al-Madeed, Somaya

doi:10.1016/j.aej.2023.12.061

Cited by 2 publications

(1 citation statement)

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“…Al-Khater addresses the issue of imbalanced and inadequate malware datasets using the Fast and Adaptive Bidimensional Empirical Mode Decomposition (FABEMD) technique [26]. FABEMD extracts different intrinsic mode Function (IMF) images to increase the training dataset.…”

Section: A Malware Detection With Image Representationmentioning

confidence: 99%

MalSSL—Self-Supervised Learning for Accurate and Label-Efficient Malware Classification

Ismail,

Hendrawan,

Rahardjo

et al. 2024

IEEE Access

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Malware classification with supervised learning requires a large dataset which needs an expensive and time-consuming labeling process. In this paper, we explore the efficacy of self-supervised learning techniques for malware classification. We propose MalSSL, a self-supervised learning-based method utilizing image representation to classify malware. MalSSL classifies unlabeled malware images using contrastive learning and data augmentation. The model is initially trained on an unlabeled Imagenette dataset as a pretext task and subsequently retrained on an unlabeled malware dataset in downstream tasks. Two downstream tasks were employed to evaluate the system: 1) Malware family classification and 2) Malware Benign classification. The obtained results include an accuracy of 98.4 % on the malware family classification experiment on the Malimg dataset and an accuracy of 96.2 % on the malware and benign dataset (Maldeb dataset). Our findings suggest that the proposed system accurately classifies malware without the need for labeled data, displaying higher accuracy compared to other self-supervised methods. This research not only contributes to advancing the state-of-the-art in malware classification but also underscores the potential of self-supervised learning methods as a viable solution for addressing the dynamic landscape of malware threats.

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Section: A Malware Detection With Image Representationmentioning

confidence: 99%