Leveraging Reinforcement Learning in Red Teaming for Advanced Ransomware Attack Simulations

TL;DR

This paper introduces a reinforcement learning-based method for simulating ransomware attacks in red teaming exercises, enabling efficient discovery of attack strategies and network vulnerabilities to improve cybersecurity defenses.

Contribution

It presents a novel RL approach for automated ransomware attack simulation, enhancing traditional red teaming with faster, more effective attack strategy discovery.

Findings

RL agent successfully identified attack paths on a 152-host network

The approach effectively evaded honeyfiles, demonstrating realistic attack capabilities

Experimental results confirmed the method's ability to find high-value targets

Abstract

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).