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

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

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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 barcoo Bridles -Stock bridles 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 Hot Chocolate of 98.4% for the malware family classification experiment on the Malimg dataset and an accuracy of 96.2% for 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.