5 papers
Zero Trust-Enabled Adaptive Incident Response using Hierarchical Reinforcement Learning
Jisoo Lee, Bhagath Singh Bondili, Harshith Doppalapudi +3
Modern enterprises increasingly operate through complex information systems (IS) enabled by cloud computing, remote and hybrid work practices, and digitally integrated supply chain…
Towards Hierarchical Cyber Defense with Large Language Models: From Planning to Execution
Harshith Doppalapudi, Nathaniel D. Bastian, Ankit Shah
An autonomous cyber defender trained with reinforcement learning (RL) is typically tied to the network on which it was trained, limiting its ability to generalize as network scale…
No One Architecture Fits All: A Cross-Environment Evaluation of Hierarchical Red Team Agents
Ayan Javeed Shaikh, Arunesh Sinha, Nathaniel D. Bastian +1
Autonomous red team agents increasingly stress-test AI-enabled cyber defenses by planning strategy and executing multistage attacks. Reinforcement learning (RL) and large language…
A Red Teaming Framework for Evaluating Robustness of AI-enabled Security Orchestration, Automation, and Response Systems
Ayan Javeed Shaikh, Nathaniel D. Bastian, Ankit Shah
AI-enabled Security Orchestration, Automation, and Response (SOAR) systems increasingly employ autonomous agents for cyber defense, yet their resilience to adaptive adversaries is…
Adapting Under Fire: Multi-Agent Reinforcement Learning for Adversarial Drift in Network Security
Emilia Rivas, Sabrina Saika, Ahtesham Bakht +3
Evolving attacks are a critical challenge for the long-term success of Network Intrusion Detection Systems (NIDS). The rise of these changing patterns has exposed the limitations o…