41 citations · 82 across the 22 of their papers we have counts for
9 papers · 1 filter
Zero-Trust Network Access (ZTNA)
Vasilios Mavroudis
Zero-Trust Network Access (ZTNA) marks a significant shift in network security by adopting a "never trust, always verify" approach. This work provides an in-depth analysis of ZTNA,…
CybORG++: An Enhanced Gym for the Development of Autonomous Cyber Agents
Harry Emerson, Liz Bates, Chris Hicks +1
CybORG++ is an advanced toolkit for reinforcement learning research focused on network defence. Building on the CAGE 2 CybORG environment, it introduces key improvements, including…
Entity-based Reinforcement Learning for Autonomous Cyber Defence
Isaac Symes Thompson, Alberto Caron, Chris Hicks +1
A significant challenge for autonomous cyber defence is ensuring a defensive agent's ability to generalise across diverse network topologies and configurations. This capability is…
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…
Environment Complexity and Nash Equilibria in a Sequential Social Dilemma
Mustafa Yasir, Andrew Howes, Vasilios Mavroudis +1
Multi-agent reinforcement learning (MARL) methods, while effective in zero-sum or positive-sum games, often yield suboptimal outcomes in general-sum games where cooperation is esse…
Mitigating Deep Reinforcement Learning Backdoors in the Neural Activation Space
Sanyam Vyas, Chris Hicks, Vasilios Mavroudis
This paper investigates the threat of backdoors in Deep Reinforcement Learning (DRL) agent policies and proposes a novel method for their detection at runtime. Our study focuses on…
A View on Out-of-Distribution Identification from a Statistical Testing Theory Perspective
Alberto Caron, Chris Hicks, Vasilios Mavroudis
We study the problem of efficiently detecting Out-of-Distribution (OOD) samples at test time in supervised and unsupervised learning contexts. While ML models are typically trained…
Online Convex Optimisation: The Optimal Switching Regret for all Segmentations Simultaneously
Stephen Pasteris, Chris Hicks, Vasilios Mavroudis +1
We consider the classic problem of online convex optimisation. Whereas the notion of static regret is relevant for stationary problems, the notion of switching regret is more appro…
Extraction Propagation
Stephen Pasteris, Chris Hicks, Vasilios Mavroudis
Running backpropagation end to end on large neural networks is fraught with difficulties like vanishing gradients and degradation. In this paper we present an alternative architect…