4 citations · 5 across the 5 of their papers we have counts for
6 papers
Learning Communication Between Heterogeneous Agents in Multi-Agent Reinforcement Learning for Autonomous Cyber Defence
Alex Popa, Adrian Taylor, Ranwa Al Mallah
Reinforcement learning techniques are being explored as solutions to the threat of cyber attacks on enterprise networks. Recent research in the field of AI in cyber security has in…
Large Language Model Integration with Reinforcement Learning to Augment Decision-Making in Autonomous Cyber Operations
Konur Tholl, François Rivest, Mariam El Mezouar +2
Reinforcement Learning (RL) has shown great potential for autonomous decision-making in the cybersecurity domain, enabling agents to learn through direct environment interaction. H…
A Comparative Evaluation of Teacher-Guided Reinforcement Learning Techniques for Autonomous Cyber Operations
Konur Tholl, Mariam El Mezouar, Ranwa Al Mallah
Autonomous Cyber Operations (ACO) rely on Reinforcement Learning (RL) to train agents to make effective decisions in the cybersecurity domain. However, existing ACO applications re…
Learning to Communicate in Multi-Agent Reinforcement Learning for Autonomous Cyber Defence
Faizan Contractor, Li Li, Ranwa Al Mallah
Popular methods in cooperative Multi-Agent Reinforcement Learning with partially observable environments typically allow agents to act independently during execution, which may lim…
Blockchain-based Monitoring for Poison Attack Detection in Decentralized Federated Learning
Ranwa Al Mallah, David Lopez
Federated Learning (FL) is a machine learning technique that addresses the privacy challenges in terms of access rights of local datasets by enabling the training of a model across…
eFedDNN: Ensemble based Federated Deep Neural Networks for Trajectory Mode Inference
Daniel Opoku Mensah, Godwin Badu-Marfo, Ranwa Al Mallah +1
As the most significant data source in smart mobility systems, GPS trajectories can help identify user travel mode. However, these GPS datasets may contain users' private informati…