A System for Interactive Examination of Learned Security Policies

TL;DR

This paper introduces an interactive system for examining learned security policies in detail, allowing users to explore policy behavior during simulated security scenarios, aiding understanding and debugging.

Contribution

It presents a novel interactive tool for inspecting and understanding security policies learned via reinforcement learning in network security contexts.

Findings

System enables detailed policy analysis during simulated attacks

Demonstrates insights into policy behavior in edge cases

Facilitates debugging and comprehension of learned policies

Abstract

We present a system for interactive examination of learned security policies. It allows a user to traverse episodes of Markov decision processes in a controlled manner and to track the actions triggered by security policies. Similar to a software debugger, a user can continue or or halt an episode at any time step and inspect parameters and probability distributions of interest. The system enables insight into the structure of a given policy and in the behavior of a policy in edge cases. We demonstrate the system with a network intrusion use case. We examine the evolution of an IT infrastructure's state and the actions prescribed by security policies while an attack occurs. The policies for the demonstration have been obtained through a reinforcement learning approach that includes a simulation system where policies are incrementally learned and an emulation system that produces statistics that drive the simulation runs.