An Empirical Analysis of Image-Based Learning Techniques for Malware Classification
This work addresses malware classification for cybersecurity applications, but it appears incremental as it builds on existing methods with a broader dataset and more techniques.
The paper tackles malware classification by applying various deep learning techniques, including MLP, CNN, LSTM, GRU, and transfer learning models like VGG-19 and ResNet152, to a larger and more diverse dataset, resulting in what they claim are the most comprehensive results published.
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.