Deep Learning CNN Implementation on Packed Malware for Cloud Cross Domain Solution Filters

This research focuses on Windows Portable Executable (PE) packed malware detection and Deep Learning (DL) using the Convolutional Neural Network (CNN) algorithm. Our primary goal is to improve the usage of DL techniques in Cybersecurity to strengthen the defenses against cyberattacks on U. S. Department of Defense (DoD) systems. According to our hypothesis, existing Cross Domain Solutions (CDSs) can be upgraded to include built-in DL-CNN algorithms for identifying well-crafted packed malware. To put this into perspective, implementing DL-CNN into the Cross Domain Solution (CDS) filter software will significantly enhance the effectiveness and detection of packed malware. CDSs are strategically positioned between unclassified and classified systems, and with DL-CNN capabilities, the CDS virus detection filter will learn to detect malware on its own, regardless of whether the malware is well-crafted, packed, or encrypted. Using our trained model, we were able to identify Windows packed PE malicious executables from Windows packed PE benign executables with an average training accuracy of 94 percent and a validation accuracy of 93 percent. Although the DL-CNN algorithm's results could be enhanced through further development and refinement using KerasTuner, this research provides a solid foundation. Our experiments were conducted on our lab computer system and in the Amazon SageMaker Studio Lab and Google Collab cloud environments.