no code implementations • 28 Nov 2022 • Melody Wolk, Andy Applebaum, Camron Dennler, Patrick Dwyer, Marina Moskowitz, Harold Nguyen, Nicole Nichols, Nicole Park, Paul Rachwalski, Frank Rau, Adrian Webster
Advancements in reinforcement learning (RL) have inspired new directions in intelligent automation of network defense.
no code implementations • 26 Sep 2020 • Gianluca Longoni, Ryan LaMothe, Jeremy Teuton, Mark Greaves, Nicole Nichols, William Smith
This paper explores the viability identifying types of engineering applications running on a cloud infrastructure configured as an HPC platform using privacy preserving features as input to statistical models.
no code implementations • 24 Apr 2020 • Loc Truong, Chace Jones, Brian Hutchinson, Andrew August, Brenda Praggastis, Robert Jasper, Nicole Nichols, Aaron Tuor
First, the success rate of backdoor poisoning attacks varies widely, depending on several factors, including model architecture, trigger pattern and regularization technique.
no code implementations • 13 Mar 2018 • Andy Brown, Aaron Tuor, Brian Hutchinson, Nicole Nichols
Deep learning has recently demonstrated state-of-the art performance on key tasks related to the maintenance of computer systems, such as intrusion detection, denial of service attack detection, hardware and software system failures, and malware detection.
1 code implementation • 2 Dec 2017 • Aaron Tuor, Ryan Baerwolf, Nicolas Knowles, Brian Hutchinson, Nicole Nichols, Rob Jasper
By treating system logs as threads of interleaved "sentences" (event log lines) to train online unsupervised neural network language models, our approach provides an adaptive model of normal network behavior.
no code implementations • 8 Nov 2017 • Nicole Nichols, Mark Raugas, Robert Jasper, Nathan Hilliard
We improve the performance of the American Fuzzy Lop (AFL) fuzz testing framework by using Generative Adversarial Network (GAN) models to reinitialize the system with novel seed files.
1 code implementation • 2 Oct 2017 • Aaron Tuor, Samuel Kaplan, Brian Hutchinson, Nicole Nichols, Sean Robinson
As a prospective filter for the human analyst, we present an online unsupervised deep learning approach to detect anomalous network activity from system logs in real time.