  Ransomware presents a significant and increasing threat to individuals and
organizations by encrypting their systems and not releasing them until a large
fee has been extracted. To bolster preparedness against potential attacks,
organizations commonly conduct red teaming exercises, which involve simulated
attacks to assess existing security measures. This paper proposes a novel
approach utilizing reinforcement learning (RL) to simulate ransomware attacks.
By training an RL agent in a simulated environment mirroring real-world
networks, effective attack strategies can be learned quickly, significantly
streamlining traditional, manual penetration testing processes. The attack
pathways revealed by the RL agent can provide valuable insights to the defense
team, helping them identify network weak points and develop more resilient
defensive measures. Experimental results on a 152-host example network confirm
the effectiveness of the proposed approach, demonstrating the RL agent's
capability to discover and orchestrate attacks on high-value targets while
evading honeyfiles (decoy files strategically placed to detect unauthorized
access).
