  Recently, cyber-attacks have been extensively seen due to the everlasting
increase of malware in the cyber world. These attacks cause irreversible damage
not only to end-users but also to corporate computer systems. Ransomware
attacks such as WannaCry and Petya specifically targets to make critical
infrastructures such as airports and rendered operational processes inoperable.
Hence, it has attracted increasing attention in terms of volume, versatility,
and intricacy. The most important feature of this type of malware is that they
change shape as they propagate from one computer to another. Since standard
signature-based detection software fails to identify this type of malware
because they have different characteristics on each contaminated computer. This
paper aims at providing an image augmentation enhanced deep convolutional
neural network (CNN) models for the detection of malware families in a
metamorphic malware environment. The main contributions of the paper's model
structure consist of three components, including image generation from malware
samples, image augmentation, and the last one is classifying the malware
families by using a convolutional neural network model. In the first component,
the collected malware samples are converted binary representation to 3-channel
images using windowing technique. The second component of the system create the
augmented version of the images, and the last component builds a classification
model. In this study, five different deep convolutional neural network model
for malware family detection is used.
