Deep Learning for Cybersecurity Classification: Utilizing Depth-Wise CNN and Attention Mechanism on VM-Obfuscated Data

Abstract

With the increasing use of sophisticated obfuscation techniques, malware detection remains a critical challenge in cybersecurity. This paper introduces a novel deep learning approach to classify malware obfuscated by virtual machine (VM) code. We specifically explore the application of depth-wise convolutional neural networks (CNNs) combined with a spatial attention mechanism to tackle VM-protected cybersecurity datasets. To address the scarcity of obfuscated malware samples, the dataset was generated using VMProtect to ensure the models were trained on real examples of modern obfuscated malware. The effectiveness of our approach is demonstrated through extensive experiments on both regular malware and obfuscated malware, where our model achieved accuracies of nearly 100% and 93.55% in classifying the regular malware and the obfuscated malware, respectively.