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

William Andreopoulos

Keywords

Malware Detection, Convolutional Neural Networks, Robust Hashing, Dataset Obfuscation, MobileNet, ResNet, DenseNet, Adversarial Attacks, Cybersecurity

Abstract

This project report provides in-depth details on the creation of a malware classification system that makes use of Convolutional Neural Networks (CNNs) that have been strengthened by data set obfuscation and strong hashing. We test many CNN architectures, including MobileNet, ResNet, and DenseNet, using rigorous hashing and obfuscation techniques on datasets. The entire pipeline is described in this research, which ranges from the gathering and preprocessing of data sets to the application of novel hashing techniques that boost overall accuracy in classification and increase resilience against malicious attacks. Parallel to this, we show that dataset obfuscation adds an additional level of protection without significantly affecting CNNs’ capacity to pick up discriminatory characteristics. The outcomes of this research confirms that if we use robust hashing it contributes heavily on detecting the malware and improves the accuracy if desgined with advancely architected CNN models. It is practical and a secure solution for malware detection as well as classification. This work could be used as a foundation for research in the area of cybersecurity especially if we have obfuscated malware images.

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Available for download on Monday, May 25, 2026

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