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Learning Cyber Defence Tactics from Scratch with Multi-Agent Reinforcement Learning
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Recent advancements in deep learning techniques have opened new possibilities for designing solutions for autonomous cyber defence. Teams of intelligent agents in computer network defence roles may reveal promising avenues to safeguard cyber and kinetic assets. In a simulated game environment, agents are evaluated on their ability to jointly mitigate attacker activity in host-based defence scenarios. Defender systems are evaluated against heuristic attackers with the goals of compromising network confidentiality, integrity, and availability. Value-based Independent Learning and Centralized Training Decentralized Execution (CTDE) cooperative Multi-Agent Reinforcement Learning (MARL) methods are compared revealing that both approaches outperform a simple multi-agent heuristic defender. This work demonstrates the ability of cooperative MARL to learn effective cyber defence tactics against varied threats.
Forward citations
Cited by 3 Pith papers
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VulnGym: Evaluating Vulnerability Management Strategies against Advanced Persistent Threats
A shared-network RL simulator shows importance-based patching cuts APT goal success far more than CVSS or centrality policies under limited defender budget.
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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.
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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.
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