Learning Cyber Defence Tactics from Scratch with Multi-Agent
  Reinforcement Learning
Jacob Wiebe Ranwa Al Mallah Li Li
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.