Data Augmentation Based Malware Detection using Convolutional Neural Networks

• URL: http://arxiv.org/abs/2010.01862v1
• Date: Mon, 5 Oct 2020 08:58:07 GMT
• Title: Data Augmentation Based Malware Detection using Convolutional Neural Networks
• Authors: Ferhat Ozgur Catak, Javed Ahmed, Kevser Sahinbas, Zahid Hussain Khand
• Abstract summary: Cyber-attacks have been extensively seen due to the increase of malware in the cyber world. The most important feature of this type of malware is that they change shape as they propagate from one computer to another. This paper aims at providing an image augmentation enhanced deep convolutional neural network models for the detection of malware families in a metamorphic malware environment.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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