An Online Framework for Adapting Security Policies in Dynamic IT Environments

Abstract

We present an online framework for learning and updating security policies in dynamic IT environments. It includes three components: a digital twin of the target system, which continuously collects data and evaluates learned policies; a system identification process, which periodically estimates system models based on the collected data; and a policy learning process that is based on reinforcement learning. To evaluate our framework, we apply it to an intrusion prevention use case that involves a dynamic IT infrastructure. Our results demonstrate that the framework automatically adapts security policies to changes in the IT infrastructure and that it outperforms a state-of-the-art method.