15 citations · 47 across the 26 of their papers we have counts for
11 papers · 1 filter
SoK: The Pitfalls of Deep Reinforcement Learning for Cybersecurity
Shae McFadden, Myles Foley, Elizabeth Bates +5
Deep Reinforcement Learning (DRL) has achieved remarkable success in domains requiring sequential decision-making, motivating its application to cybersecurity problems. However, tr…
Beyond Rewards in Reinforcement Learning for Cyber Defence
Elizabeth Bates, Chris Hicks, Vasilios Mavroudis
Recent years have seen an explosion of interest in autonomous cyber defence agents trained to defend computer networks using deep reinforcement learning. These agents are typically…
Poisoning Attacks on LLMs Require a Near-constant Number of Poison Samples
Alexandra Souly, Javier Rando, Ed Chapman +10
Poisoning attacks can compromise the safety of large language models (LLMs) by injecting malicious documents into their training data. Existing work has studied pretraining poisoni…
DRMD: Deep Reinforcement Learning for Malware Detection under Concept Drift
Shae McFadden, Myles Foley, Mario D'Onghia +4
Malware detection in real-world settings must deal with evolving threats, limited labeling budgets, and uncertain predictions. Traditional classifiers, without additional mechanism…
Beyond Training-time Poisoning: Component-level and Post-training Backdoors in Deep Reinforcement Learning
Sanyam Vyas, Alberto Caron, Chris Hicks +2
Deep Reinforcement Learning (DRL) systems are increasingly used in safety-critical applications, yet their security remains severely underexplored. This work investigates backdoor…
On Efficient Bayesian Exploration in Model-Based Reinforcement Learning
Alberto Caron, Chris Hicks, Vasilios Mavroudis
In this work, we address the challenge of data-efficient exploration in reinforcement learning by examining existing principled, information-theoretic approaches to intrinsic motiv…
Towards Causal Model-Based Policy Optimization
Alberto Caron, Vasilios Mavroudis, Chris Hicks
Real-world decision-making problems are often marked by complex, uncertain dynamics that can shift or break under changing conditions. Traditional Model-Based Reinforcement Learnin…
Less is more? Rewards in RL for Cyber Defence
Elizabeth Bates, Chris Hicks, Vasilios Mavroudis
The last few years have seen an explosion of interest in autonomous cyber defence agents based on deep reinforcement learning. Such agents are typically trained in a cyber gym envi…
An Attentive Graph Agent for Topology-Adaptive Cyber Defence
Ilya Orson Sandoval, Isaac Symes Thompson, Vasilios Mavroudis +1
As cyber threats grow increasingly sophisticated, reinforcement learning (RL) is emerging as a promising technique to create intelligent and adaptive cyber defense systems. However…
Inherently Interpretable and Uncertainty-Aware Models for Online Learning in Cyber-Security Problems
Benjamin Kolicic, Alberto Caron, Chris Hicks +1
In this paper, we address the critical need for interpretable and uncertainty-aware machine learning models in the context of online learning for high-risk industries, particularly…
Fairness with Exponential Weights
Stephen Pasteris, Chris Hicks, Vasilios Mavroudis
Motivated by the need to remove discrimination in certain applications, we develop a meta-algorithm that can convert any efficient implementation of an instance of Hedge (or equiva…