An Empirical Analysis of Image-Based Learning Techniques for Malware Classification

• URL: http://arxiv.org/abs/2103.13827v1
• Date: Wed, 24 Mar 2021 16:10:05 GMT
• Title: An Empirical Analysis of Image-Based Learning Techniques for Malware Classification
• Authors: Pratikkumar Prajapati and Mark Stamp
• Abstract summary: In this paper, we consider malware classification using deep learning techniques and image-based features. We employ a wide variety of deep learning techniques, including multilayer perceptrons (MLP), convolutional neural networks (CNN), long short-term memory (LSTM), and gated recurrent units (GRU)
• Score: 4.111899441919165
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we consider malware classification using deep learning techniques and image-based features. We employ a wide variety of deep learning techniques, including multilayer perceptrons (MLP), convolutional neural networks (CNN), long short-term memory (LSTM), and gated recurrent units (GRU). Amongst our CNN experiments, transfer learning plays a prominent role specifically, we test the VGG-19 and ResNet152 models. As compared to previous work, the results presented in this paper are based on a larger and more diverse malware dataset, we consider a wider array of features, and we experiment with a much greater variety of learning techniques. Consequently, our results are the most comprehensive and complete that have yet been published.

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