41 citations · 54 across the 10 of their papers we have counts for
10 papers
REPLICANT: Learning Policies for Evading and Hardening Malware Detectors
Shae McFadden, Ilias Tsingenopoulos, Mario D'Onghia +5
To determine the real-world effectiveness of machine learning based malware detection, it is vital to evaluate its robustness against highly capable adversaries. However, state-of-…
Building Better Environments for Autonomous Cyber Defence
Chris Hicks, Elizabeth Bates, Shae McFadden +12
In November 2025, the authors ran a workshop on the topic of what makes a good reinforcement learning (RL) environment for autonomous cyber defence (ACD). This paper details the kn…
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…
Clutch Control: An Attention-based Combinatorial Bandit for Efficient Mutation in JavaScript Engine Fuzzing
Myles Foley, Sergio Maffeis, Muhammad Fakhrur Rozi +1
JavaScript engines are widely used in web browsers, PDF readers, and server-side applications. The rise in concern over their security has led to the development of several targete…
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…
APIRL: Deep Reinforcement Learning for REST API Fuzzing
Myles Foley, Sergio Maffeis
REST APIs have become key components of web services. However, they often contain logic flaws resulting in server side errors or security vulnerabilities. HTTP requests are used as…
Autonomous Network Defence using Reinforcement Learning
Myles Foley, Chris Hicks, Kate Highnam +1
In the network security arms race, the defender is significantly disadvantaged as they need to successfully detect and counter every malicious attack. In contrast, the attacker nee…
Canaries and Whistles: Resilient Drone Communication Networks with (or without) Deep Reinforcement Learning
Chris Hicks, Vasilios Mavroudis, Myles Foley +3
Communication networks able to withstand hostile environments are critically important for disaster relief operations. In this paper, we consider a challenging scenario where drone…
Inroads into Autonomous Network Defence using Explained Reinforcement Learning
Myles Foley, Mia Wang, Zoe M +2
Computer network defence is a complicated task that has necessitated a high degree of human involvement. However, with recent advancements in machine learning, fully autonomous net…
Matching Pairs: Attributing Fine-Tuned Models to their Pre-Trained Large Language Models
Myles Foley, Ambrish Rawat, Taesung Lee +3
The wide applicability and adaptability of generative large language models (LLMs) has enabled their rapid adoption. While the pre-trained models can perform many tasks, such model…