Scalable Edge Blocking Algorithms for Defending Active Directory Style Attack Graphs

TL;DR

This paper introduces scalable algorithms for defending Active Directory attack graphs by blocking edges, leveraging the graphs' tree-like structure and novel parameters to efficiently minimize attacker success rates.

Contribution

It presents new fixed parameter algorithms and a reinforcement learning approach tailored for large AD graphs, improving scalability and practical applicability.

Findings

Algorithms scale to tens of thousands of nodes.

Tree decomposition based method provides near-optimal solutions.

Kernelization technique reduces problem size significantly.

Abstract

Active Directory (AD) is the default security management system for Windows domain networks. An AD environment naturally describes an attack graph where nodes represent computers/accounts/security groups, and edges represent existing accesses/known exploits that allow the attacker to gain access from one node to another. Motivated by practical AD use cases, we study a Stackelberg game between one attacker and one defender. There are multiple entry nodes for the attacker to choose from and there is a single target (Domain Admin). Every edge has a failure rate. The attacker chooses the attack path with the maximum success rate. The defender can block a limited number of edges (i.e., revoke accesses) from a set of blockable edges, limited by budget. The defender's aim is to minimize the attacker's success rate. We exploit the tree-likeness of practical AD graphs to design scalable algorithms. We propose two novel methods that combine theoretical fixed parameter analysis and practical optimisation techniques. For graphs with small tree widths, we propose a tree decomposition based dynamic program. We then propose a general method for converting tree decomposition based dynamic programs to reinforcement learning environments, which leads to an anytime algorithm that scales better, but loses the optimality guarantee. For graphs with small numbers of non-splitting paths (a parameter we invent specifically for AD graphs), we propose a kernelization technique that significantly downsizes the model, which is then solved via mixed-integer programming. Experimentally, our algorithms scale to handle synthetic AD graphs with tens of thousands of nodes.