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Self-Supervised Vision Transformers for Malware Detection
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DOI：10.1109/ACCESS.2022.3206445.png)
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Abstract
En 中文
Malware detection plays a crucial role in cyber-security with the increase in malware growth and advancements in cyber-attacks. Previously unseen malware which is not determined by security vendors are often used in these attacks and it is becoming inevitable to find a solution that can self-learn from unlabeled sample data. This paper presents SHERLOCK, a self-supervision based deep learning model to detect malware based on the Vision Transformer (ViT) architecture. SHERLOCK is a novel malware detection method which learns unique features to differentiate malware from benign programs with the use of image-based binary representation. Experimental results using 1.2 million Android applications across a hierarchy of 47 types and 696 families, shows that self-supervised learning can achieve an accuracy of 97% for the binary classification of malware which is higher than existing state-of-the-art techniques. Our proposed model is also able to outperform state-of-the-art techniques for multi-class malware classification of types and family with macro-F1 score of.497 and.491 respectively.
Keywords:
Malware
Feature extraction
Deep learning
Self-supervised learning
Operating systems
Gray-scale
Visualization
Androids
Self-supervised learning
deep learning
malware detection
Android security
Journal
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3.6
Papers:
9.8W
Citations:
29.4W
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