6 papers
DEFENGRAPH: Knowledge Graph-Enhanced LLMs for Blue Team Cyber Defense
Zhen Wang, Kristen Moore, Qin Wang +7
Large Language Models (LLMs) show promise for supporting decision-making in cybersecurity, but their reliability in high-stakes, time-evolving environments remains limited due to h…
Unveiling the Black Box: A Multi-Layer Framework for Explaining Reinforcement Learning-Based Cyber Agents
Diksha Goel, Kristen Moore, Jeff Wang +2
Reinforcement Learning (RL) agents are increasingly used to simulate sophisticated cyberattacks, but their decision-making processes remain opaque, hindering trust, debugging, and…
TempoNet: Learning Realistic Communication and Timing Patterns for Network Traffic Simulation
Kristen Moore, Diksha Goel, Cody James Christopher +5
Realistic network traffic simulation is critical for evaluating intrusion detection systems, stress-testing network protocols, and constructing high-fidelity environments for cyber…
Pensieve Grader: An AI-Powered, Ready-to-Use Platform for Effortless Handwritten STEM Grading
Yoonseok Yang, Minjune Kim, Marlon Rondinelli +1
Grading handwritten, open-ended responses remains a major bottleneck in large university STEM courses. We introduce Pensieve (https://www.pensieve.co), an AI-assisted grading platf…
CyberAlly: Leveraging LLMs and Knowledge Graphs to Empower Cyber Defenders
Minjune Kim, Jeff Wang, Kristen Moore +7
The increasing frequency and sophistication of cyberattacks demand innovative approaches to strengthen defense capabilities. Training on live infrastructure poses significant risks…
CAMP in the Odyssey: Provably Robust Reinforcement Learning with Certified Radius Maximization
Derui Wang, Kristen Moore, Diksha Goel +8
Deep reinforcement learning (DRL) has gained widespread adoption in control and decision-making tasks due to its strong performance in dynamic environments. However, DRL agents are…