Publication Date

Spring 2025

Degree Type

Master's Project

Degree Name

Master of Science in Computer Science (MSCS)

Department

Computer Science

First Advisor

Fabio Di Troia

Second Advisor

Navrati Saxena

Third Advisor

Thomas Austin

Keywords

Convolutional Neural Network, PixelCNN, Malware, Deep Learning, Machine Learning

Abstract

Malware poses a serious threat to both data privacy and system security. With the wide variety of malware families and the surge in cyber-attacks, the accurate classification of malware is crucial for building effective detection and prevention systems. In recent years, deep learning (DL) methods in computer vision have shown promise in classifying malware by converting malware files into visual representations and applying DL algorithms to classify the resulting images. Among the different approaches to malware family classification, image-based methods have gained significant interest. This research focuses on leveraging DL techniques for image-based classification of malware. The success of identifying malicious files largely depends on the quality and size of the training dataset, as well as its authenticity. However, a major obstacle in utilizing DL for malware detection is the shortage of training data. To address this challenge, this project utilizes the Pixel Convolutional Neural Network (PixelCNN) architecture to generate synthetic, class-specific malware images pixel by pixel, capturing intricate details of the images. Additionally, a CNN-based DL model was developed, demonstrating that training with a mixture of at least 15% real malware data alongside the generated images resulted in notable multi-class classification accuracy.

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Available for download on Monday, May 25, 2026

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