2 citations · 3 across the 11 of their papers we have counts for
4 papers · 1 filter
RawMal-TF: Raw Malware Dataset Labeled by Type and Family
David Bálik, Martin Jureček, 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…