Topic 4.5 of Reinforcement Learning in Cybersecurity

Securing Network Traffic, and Beyond: Rate-Limiting, Moving Target Defence, Patch Scheduling

A second worked Q-learning agent for adaptive rate-limiting with a continuous, discretised state space, why large state spaces need Deep Q-Networks instead of a table, moving target defence as an active research area, and the risk of reward-hacking.