Publication Date

Fall 2024

Degree Type

Master's Project

Degree Name

Master of Science in Computer Science (MSCS)

Department

Computer Science

First Advisor

Mark Stamp

Second Advisor

Katerina Potika

Third Advisor

Genya Ishigaki

Keywords

Malware Detection, Convolutional Neural Networks (CNN), QR Codes, Aztec Codes, Executable Analysis.

Abstract

In recent years, the use of image-based techniques for malware detection has gained prominence, with numerous studies demonstrating the efficacy of deep learning approaches such as convolutional neural networks (CNNs) in classifying images derived from executable files. In this paper, we consider an innovative method that relies on an image conversion process that consists of transforming executable files into QR and Aztec codes. These codes capture structural patterns in a format that may enhance the learning capabilities of CNNs. We design and implement CNN architectures tailored to the unique properties of these codes and apply them to a comprehensive analysis involving two extensive malware datasets, alongside a significant corpus of benign executables. Our results, which surpass those of comparable studies, suggest that the choice of image conversion strategy is crucial, and that using QR and Aztec codes offers a promising direction for future research in malware detection.

Available for download on Saturday, January 24, 2026

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