Deep Learning Applied to Imbalanced Malware Datasets Classification

In the current day, the evolution and exponential proliferation of malware involve modifications and camouflage of their structure through techniques like obfuscation, polymorphism, metamorphism, and encryption. With the advancements in deep learning, methods such as convolutional neural networks (CNN) have emerged as potent tools for deciphering intricate patterns within this malicious software. The present research uses the capacity of CNN to learn the global structure of the code converted to an RGB or grayscale image and decipher the patterns present in the malware datasets generated from these images. The study explores fine-tuning techniques, including bicubic interpolation, ReduceLROnPlateau, and class weight estimation, in order to generalize the model and reduce the risk of overfitting for malware that uses evasion techniques against classification. Taking advantage of transfer learning and the MobileNet architecture, we created a MobileNet fine-tuning (FT) model. The application of this new model in four datasets, including Microsoft Big 2015, Malimg, MaleVis, and a new Fusion dataset, achieved 98.71%, 99.08%, 96.04%, and 98.04% accuracy, respectively, which underscores the robustness of the proposed model. The Fusion dataset is a combination of the first three datasets, consisting of a set of 32,601 known malware image files representing a mix of 59 different families. Despite the success, the study reveals performance deterioration with an increase in the number of malware families, highlighting the need for further exploration into the limits of CNNs in malware classification.