4 citations · 6 across the 11 of their papers we have counts for
4 papers · 1 filter
Hierarchical Multi-agent Reinforcement Learning for Cyber Network Defense
Aditya Vikram Singh, Ethan Rathbun, Emma Graham +4
Recent advances in multi-agent reinforcement learning (MARL) have created opportunities to solve complex real-world tasks. Cybersecurity is a notable application area, where defend…
Adversarial Inception Backdoor Attacks against Reinforcement Learning
Ethan Rathbun, Alina Oprea, Christopher Amato
Recent works have demonstrated the vulnerability of Deep Reinforcement Learning (DRL) algorithms against training-time, backdoor poisoning attacks. The objectives of these attacks…
SleeperNets: Universal Backdoor Poisoning Attacks Against Reinforcement Learning Agents
Ethan Rathbun, Christopher Amato, Alina Oprea
Reinforcement learning (RL) is an actively growing field that is seeing increased usage in real-world, safety-critical applications -- making it paramount to ensure the robustness…
Distilling Adversarial Robustness Using Heterogeneous Teachers
Jieren Deng, Aaron Palmer, Rigel Mahmood +4
Achieving resiliency against adversarial attacks is necessary prior to deploying neural network classifiers in domains where misclassification incurs substantial costs, e.g., self-…