152 citations · 236 across the 10 of their papers we have counts for
10 papers
AI Security Priorities: A Field-Wide Agenda
Gil Gekker, Rachel Steratore, Everett Smith +9
As AI systems are rapidly integrated into critical economic, governmental, and national security functions, the gap between AI adoption and AI security readiness continues to widen…
Proactive Hardening of LLM Defenses with HASTE
Henry Chen, Victor Aranda, Samarth Keshari +2
Prompt-based attack techniques are one of the primary challenges in securely deploying and protecting LLM-based AI systems. LLM inputs are an unbounded, unstructured space. Consequ…
Practice-Informed, Practice-Ready: An AI security incident taxonomy
Lukas Bieringer, Sean McGregor, Nicole Nichols +7
With the increasing prevalence of AI systems, several real-world AI security incidents have been reported. However, despite forthcoming legal mandates, the reporting and collection…
Machine Learning Algorithms for Active Monitoring of High Performance Computing as a Service (HPCaaS) Cloud Environments
Gianluca Longoni, Ryan LaMothe, Jeremy Teuton +3
Cloud computing provides ubiquitous and on-demand access to vast reconfigurable resources that can meet any computational need. Many service models are available, but the Infrastru…
Systematic Evaluation of Backdoor Data Poisoning Attacks on Image Classifiers
Loc Truong, Chace Jones, Brian Hutchinson +5
Backdoor data poisoning attacks have recently been demonstrated in computer vision research as a potential safety risk for machine learning (ML) systems. Traditional data poisoning…
Projecting Trouble: Light Based Adversarial Attacks on Deep Learning Classifiers
Nicole Nichols, Robert Jasper
This work demonstrates a physical attack on a deep learning image classification system using projected light onto a physical scene. Prior work is dominated by techniques for creat…
Recurrent Neural Network Attention Mechanisms for Interpretable System Log Anomaly Detection
Andy Brown, Aaron Tuor, Brian Hutchinson +1
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 att…
Recurrent Neural Network Language Models for Open Vocabulary Event-Level Cyber Anomaly Detection
Aaron Tuor, Ryan Baerwolf, Nicolas Knowles +3
Automated analysis methods are crucial aids for monitoring and defending a network to protect the sensitive or confidential data it hosts. This work introduces a flexible, powerful…
Faster Fuzzing: Reinitialization with Deep Neural Models
Nicole Nichols, Mark Raugas, Robert Jasper +1
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 fi…
Deep Learning for Unsupervised Insider Threat Detection in Structured Cybersecurity Data Streams
Aaron Tuor, Samuel Kaplan, Brian Hutchinson +2
Analysis of an organization's computer network activity is a key component of early detection and mitigation of insider threat, a growing concern for many organizations. Raw system…