Automated Attack Response Through Reinforcement Learning

In the current digital landscape, the increasing sophistication and frequency of cyberattacks pose significant challenges. Traditional manual responses, while somewhat effective, are labor-intensive and prone to human error, leading to increased operational costs and potential vulnerabilities due to alert fatigue. The global financial implications of these cyberattacks highlight the urgent need for more efficient, automated solutions in responding to attacks. This work introduces a framework designed to train and deploy Reinforcement Learning (RL) agents to automate attack response in the context of a single host machine. We leverage real-world data from controlled cyberattacks to simulate realistic attack scenarios and train a defender agent using a high-fidelity simulation. We then deploy the defender agent in real-world settings, effectively countering cyber threats. This approach offers a promising solution to the challenges of timely and efficient cyber defense, reducing human intervention and ensuring rapid, accurate responses to emerging threats.