The modern-day Cyber field continues to be plagued with innumerable forms of malware that are created on a massive scale. The ever-changing nature of malware threats combined with the obfuscation techniques used by attackers creates the need for effective methods of malware classification. As of 2018, an average of one million new forms of malware are created world-wide each day, which raises the question of how to combat these attacks. While most antiviruses scan the integrity and composition of files in the system, we propose a new approach to Cyber Defense. As a replacement for standard file scans, we advocate the conversion of the malware binary into a grayscale image for classification and visualization. As discovered by previous research, different types of malware families tend to display similar characteristics and binary patterns between the various malware files in each family. Since there are similarities between the various files of malware in each family, the idea arose to augment these groups with synthetic data generated from a Generative Adversarial Network (GAN). The idea of a constant stream of generated malware leads to the hypothesis that by adding synthetic data based on each family to each family the images are generated from will create a higher learning rate from the Deep Convolutional Neural Network (DCNN). Various architectures of the DCNN will be used as assessments that benchmark each architectures’ learning rate before and after the augmentation.
Wavelet transformation has become a cutting edge and promising approach in the field of image and signal processing. A wavelet is a waveform of effectively limited duration that has an average value of zero. Wavelet analysis is done by breaking up the signal into shifted and scaled versions of the original signal. The key advantage of a wavelet is that it is capable of revealing smaller changes, trends, and breakdown points that are not revealed by other techniques such as Fourier analysis. The phenomenon of polarization has been studied for quite some time and is a very useful tool for target detection and tracking. Long Wave Infrared (LWIR) polarization is beneficial for detecting camouflaged objects and is a useful approach when identifying and distinguishing manmade objects from natural clutter. In addition, the Stokes Polarization Parameters, which are calculated from 0°, 45°, 90°, 135° right circular, and left circular intensity measurements, provide spatial orientations of target features and suppress natural features. In this paper, we propose a wavelet-based polarimetry analysis (WPA) method to analyze Long Wave Infrared Polarimetry Imagery to discriminate targets such as dismounts and vehicles from background clutter. These parameters can be used for image thresholding and segmentation. Experimental results show the wavelet-based polarimetry analysis is efficient and can be used in a wide range of applications such as change detection, shape extraction, target recognition, and feature-aided tracking.