Using 3D-VGG-16 and 3D-Resnet-18 deep learning models and FABEMD techniques in the detection of malware

Abstract

Currently, the detection of malware to prevent cybersecurity breaches is a raising a concern for millions of people around the globe. Even with the most recent updates, antivirus software can leave computer users vulnerable to malware threats and attacks due to the rapid release of new malware. As a result, conventional antivirus software is frequently incapable of identifying novel forms of malware until their unique signatures have been added to the software's database. To protect systems, there is necessity in the current technology field to develop innovative techniques for detecting unidentified malware, particularly during the initial phases of malware deployment. The objective is to address the issue of imbalanced and inadequate malware dataset through the utilization of the fast and adaptive bidirectional empirical mode decomposition (FABEMD) technique. While exploring the Malimg and MaleVis datasets, promising results were reached in reference to accuracy, precision, recall, and F1-score. The experiment demonstrated the accuracy of two distinct 3D architectures, namely 3D VGG-16 and 3D Resnet-18, on two separate datasets: Malimg dataset and MaleVis dataset. The accuracy achieved for VGG-16 was 96.14% and 98.60% for the Malimg and the MaleVis dataset, respectively. In regards to the Resnet-18 architecture, it demonstrated high accuracy rates of 99.64% and 99.46% for the Malimg and MaleVis datasets, respectively.

Keywords

• Malware classification
• FABEMD
• 3D VGG-16
• 3D Resnet-18