331 citations · 619 across the 15 of their papers we have counts for
23 papers
A Practical Guide for Evaluating LLMs and LLM-Reliant Systems
Ethan M. Rudd, Christopher Andrews, Philip Tully
Recent advances in generative AI have led to remarkable interest in using systems that rely on large language models (LLMs) for practical applications. However, meaningful evaluati…
Cross-Subject Deep Transfer Models for Evoked Potentials in Brain-Computer Interface
Chad Mello, Troy Weingart, Ethan M. Rudd
Brain Computer Interface (BCI) technologies have the potential to improve the lives of millions of people around the world, whether through assistive technologies or clinical diagn…
Transformers for End-to-End InfoSec Tasks: A Feasibility Study
Ethan M. Rudd, Mohammad Saidur Rahman, Philip Tully
In this paper, we assess the viability of transformer models in end-to-end InfoSec settings, in which no intermediate feature representations or processing steps occur outside the…
Efficient Malware Analysis Using Metric Embeddings
Ethan M. Rudd, David Krisiloff, Scott Coull +3
In this paper, we explore the use of metric learning to embed Windows PE files in a low-dimensional vector space for downstream use in a variety of applications, including malware…
SOREL-20M: A Large Scale Benchmark Dataset for Malicious PE Detection
Richard Harang, Ethan M. Rudd
In this paper we describe the SOREL-20M (Sophos/ReversingLabs-20 Million) dataset: a large-scale dataset consisting of nearly 20 million files with pre-extracted features and metad…
Training Transformers for Information Security Tasks: A Case Study on Malicious URL Prediction
Ethan M. Rudd, Ahmed Abdallah
Machine Learning (ML) for information security (InfoSec) utilizes distinct data types and formats which require different treatments during optimization/training on raw data. In th…