  This paper addresses a significant gap in Autonomous Cyber Operations (ACO)
literature: the absence of effective edge-blocking ACO strategies in dynamic,
real-world networks. It specifically targets the cybersecurity vulnerabilities
of organizational Active Directory (AD) systems. Unlike the existing literature
on edge-blocking defenses which considers AD systems as static entities, our
study counters this by recognizing their dynamic nature and developing advanced
edge-blocking defenses through a Stackelberg game model between attacker and
defender. We devise a Reinforcement Learning (RL)-based attack strategy and an
RL-assisted Evolutionary Diversity Optimization-based defense strategy, where
the attacker and defender improve each other strategy via parallel gameplay. To
address the computational challenges of training attacker-defender strategies
on numerous dynamic AD graphs, we propose an RL Training Facilitator that
prunes environments and neural networks to eliminate irrelevant elements,
enabling efficient and scalable training for large graphs. We extensively train
the attacker strategy, as a sophisticated attacker model is essential for a
robust defense. Our empirical results successfully demonstrate that our
proposed approach enhances defender's proficiency in hardening dynamic AD
graphs while ensuring scalability for large-scale AD.
