Transfer Learning for Image-Based Malware Classification
This addresses malware detection for cybersecurity, but the results are incremental as they show mixed performance compared to a baseline method.
The paper tackled malware classification by converting executables to images and using transfer learning with deep learning models, but found that a simple k-NN method outperformed it in standard experiments, though deep learning showed better generalization in zero-day scenarios.
In this paper, we consider the problem of malware detection and classification based on image analysis. We convert executable files to images and apply image recognition using deep learning (DL) models. To train these models, we employ transfer learning based on existing DL models that have been pre-trained on massive image datasets. We carry out various experiments with this technique and compare its performance to that of an extremely simple machine learning technique, namely, k-nearest neighbors (\kNN). For our k-NN experiments, we use features extracted directly from executables, rather than image analysis. While our image-based DL technique performs well in the experiments, surprisingly, it is outperformed by k-NN. We show that DL models are better able to generalize the data, in the sense that they outperform k-NN in simulated zero-day experiments.