REVIEW 7 cited by
On Autonomous Agents in a Cyber Defence Environment
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Autonomous Cyber Defence is required to respond to high-tempo cyber-attacks. To facilitate the research in this challenging area, we explore the utility of the autonomous cyber operation environments presented as part of the Cyber Autonomy Gym for Experimentation (CAGE) Challenges, with a specific focus on CAGE Challenge 2. CAGE Challenge 2 required a defensive Blue agent to defend a network from an attacking Red agent. We provide a detailed description of the this challenge and describe the approaches taken by challenge participants. From the submitted agents, we identify four classes of algorithms, namely, Single- Agent Deep Reinforcement Learning (DRL), Hierarchical DRL, Ensembles, and Non-DRL approaches. Of these classes, we found that the hierarchical DRL approach was the most capable of learning an effective cyber defensive strategy. Our analysis of the agent policies identified that different algorithms within the same class produced diverse strategies and that the strategy used by the defensive Blue agent varied depending on the strategy used by the offensive Red agent. We conclude that DRL algorithms are a suitable candidate for autonomous cyber defence applications.
Forward citations
Cited by 7 Pith papers
-
Beyond Rewards in Reinforcement Learning for Cyber Defence
Sparse, goal-aligned rewards outperform dense engineered rewards for training cyber-defence RL agents, but the advantage depends on the evaluation metric and is not uniformly confirmed in the more complex CAGE environment.
-
Interpreting Agent Behaviors in Reinforcement-Learning-Based Cyber-Battle Simulation Platforms
By tracking per-host ground-truth states, the authors measure how often each CAGE Challenge 2 action actually changes a host's state, finding that top agents waste many actions and that decoys correlate with fewer suc...
-
Distilling Knowledge from Large Language Models into Lightweight Reinforcement Learning Agents for Autonomous Cyber Operations
LLM cyber-defense policies, obtained by prompt engineering alone, can be behavior-cloned into a 64,910-parameter RL agent that matches a heavily trained PPO baseline in the CybORG simulator.
-
Strategic Cyber Defense via Reinforcement Learning-Guided Combinatorial Auctions
RL Q-values are used as bids in a learned combinatorial auction that allocates defensive actions in the DARPA CAGE 2 simulation, giving revenue near an oracle and allocations loosely aligned with defender activity.
-
Learning to Communicate in Multi-Agent Reinforcement Learning for Autonomous Cyber Defence
Adapting the DIAL communication algorithm to CybORG, the authors report that one-bit messaging between defenders beats a global-state QMix baseline in harder simulated cyber attack scenarios.
-
General Autonomous Cybersecurity Defense: Learning Robust Policies for Dynamic Topologies and Diverse Attackers
A graph-neural-network plus optimal-transport agent trained on procedurally generated networks generalizes across topology changes and two attacker types in the CAGE 2 simulation.
-
Nash Q-Network for Multi-Agent Cybersecurity Simulation
A MARL variant that trains agents by aligning their policies with Nash equilibria of a centralized critic's joint Q-values, demonstrated on the CybORG CC2 cyber-defense scenario.
Discussion (0). Continue with ORCID to comment.