Publication Date

Fall 2023

Degree Type

Master's Project

Degree Name

Master of Science in Data Science (MSDS)


Computer Science

First Advisor

Fabio Di Troia

Second Advisor

Rula Khayrallah

Third Advisor

Katerina Potika


Malware, machine learning, convolutional neural network, convolutional recurrent neural network


In this study, we delve into the realm of malware detection and classification, leveraging the capabilities of different Convolutional Neural Networks (CNNs). Our approach involves transforming executable files into image formats and subsequently applying advanced CNN techniques for image recognition. The study emphasizes the use of two distinct CNN architectures: a traditional CNN model and a modified CNN variant known as a convolutional recurrent neural network (CRNN), each with unique structural and functional attributes. To effectively train these models, we adopt a transfer learning strategy, utilizing pre-existing CNN models that have been extensively trained on large-scale image datasets. This methodology allows us to harness the power of deep learning in recognizing complex patterns in image-based data representations of executable files. Our experimental analysis offers a comprehensive comparison between the traditional and modified CNN models. We evaluate their performance in terms of accuracy, generalization ability, and efficiency in detecting and classifying malware. The findings of this study provide insightful conclusions about the strengths and limitations of CNN and CRNN in the context of malware detection. After experimentation, CRNN can produce similar accuracy results to CNN despite design differences in both models.

Available for download on Friday, December 20, 2024