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.
Recommended Citation
Khadilkar, Atharva, "Malware Detection Using QR and Aztec Code Representations" (2024). Master's Projects. 1462.
https://scholarworks.sjsu.edu/etd_projects/1462