1 citations · 1 across the 6 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
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…
Optimizing Cyber Defense in Dynamic Active Directories through Reinforcement Learning
Diksha Goel, Kristen Moore, Mingyu Guo +3
This paper addresses a significant gap in Autonomous Cyber Operations (ACO) literature: the absence of effective edge-blocking ACO strategies in dynamic, real-world networks. It sp…