6 papers
CAM-Guided Saliency Cutout and Image-Based Malware Classification
Yasaman Ebrahimi, Martin Jurecek, Mark Stamp
Dropout regularization is commonly used to reduce overfitting by removing parts of a neural network during training. For Convolutional Neural Networks (CNN), cutouts serve a somewh…
RawMal-TF: Raw Malware Dataset Labeled by Type and Family
David Bálik, Martin JureÄek, Mark Stamp
This work addresses the challenge of malware classification using machine learning by developing a novel dataset labeled at both the malware type and family levels. Raw binaries we…
Transforming Chatbot Text: A Sequence-to-Sequence Approach
Natesh Reddy, Mark Stamp
Due to advances in Large Language Models (LLMs) such as ChatGPT, the boundary between human-written text and AI-generated text has become blurred. Nevertheless, recent work has dem…
Energy Considerations for Large Pretrained Neural Networks
Leo Mei, Mark Stamp
Increasingly complex neural network architectures have achieved phenomenal performance. However, these complex models require massive computational resources that consume substanti…
Multimodal Techniques for Malware Classification
Jonathan Jiang, Mark Stamp
The threat of malware is a serious concern for computer networks and systems, highlighting the need for accurate classification techniques. In this research, we experiment with mul…
Malware Classification using a Hybrid Hidden Markov Model-Convolutional Neural Network
Ritik Mehta, Olha Jureckova, Mark Stamp
The proliferation of malware variants poses a significant challenges to traditional malware detection approaches, such as signature-based methods, necessitating the development of…