27 citations · 81 across the 32 of their papers we have counts for
6 papers · 1 filter
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…
Lempel-Ziv Networks
Rebecca Saul, Mohammad Mahmudul Alam, John Hurwitz +3
Sequence processing has long been a central area of machine learning research. Recurrent neural nets have been successful in processing sequences for a number of tasks; however, th…
Deploying Convolutional Networks on Untrusted Platforms Using 2D Holographic Reduced Representations
Mohammad Mahmudul Alam, Edward Raff, Tim Oates +1
Due to the computational cost of running inference for a neural network, the need to deploy the inferential steps on a third party's compute environment or hardware is common. If t…
Marvolo: Programmatic Data Augmentation for Practical ML-Driven Malware Detection
Michael D. Wong, Edward Raff, James Holt +1
Data augmentation has been rare in the cyber security domain due to technical difficulties in altering data in a manner that is semantically consistent with the original data. This…
Proceedings of the Artificial Intelligence for Cyber Security (AICS) Workshop at AAAI 2022
James Holt, Edward Raff, Ahmad Ridley +7
The workshop will focus on the application of AI to problems in cyber security. Cyber systems generate large volumes of data, utilizing this effectively is beyond human capabilitie…
Out of Distribution Data Detection Using Dropout Bayesian Neural Networks
Andre T. Nguyen, Fred Lu, Gary Lopez Munoz +3
We explore the utility of information contained within a dropout based Bayesian neural network (BNN) for the task of detecting out of distribution (OOD) data. We first show how pre…