Practical Fixed-Parameter Algorithms for Defending Active Directory Style Attack Graphs

Author:

Guo Mingyu,Li Jialiang,Neumann Aneta,Neumann Frank,Nguyen Hung

Abstract

Active Directory is the default security management system for Windows domain networks. We study the shortest path edge interdiction problem for defending Active Directory style attack graphs. The problem is formulated as a Stackelberg game between one defender and one attacker. The attack graph contains one destination node and multiple entry nodes. The attacker's entry node is chosen by nature. The defender chooses to block a set of edges limited by his budget. The attacker then picks the shortest unblocked attack path. The defender aims to maximize the expected shortest path length for the attacker, where the expectation is taken over entry nodes. We observe that practical Active Directory attack graphs have small maximum attack path length and are structurally close to trees. We first show that even if the maximum attack path length is a constant, the problem is still w[1]-hard with respect to the defender's budget. Having a small maximum attack path length and a small budget is not enough to design fixed-parameter algorithms. If we further assume that the number of entry nodes is small, then we derive a fixed-parameter tractable algorithm. We then propose two other fixed-parameter algorithms by exploiting the tree-like features. One is based on tree decomposition and requires a small tree width. The other assumes a small number of splitting nodes (nodes with multiple out-going edges). Finally, the last algorithm is converted into a graph convolutional neural network based heuristic, which scales to larger graphs with more splitting nodes.

Publisher

Association for the Advancement of Artificial Intelligence (AAAI)

Subject

General Medicine

Cited by 11 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Hardening Active Directory Graphs via Evolutionary Diversity Optimization based Policies;ACM Transactions on Evolutionary Learning and Optimization;2024-08-12

2. Demo: Synthesizing Realistic Enterprise Active Directory Attack Graphs with ADSynth;Proceedings of the ACM SIGCOMM 2024 Conference: Posters and Demos;2024-08-04

3. Optimizing Cyber Response Time on Temporal Active Directory Networks Using Decoys;Proceedings of the Genetic and Evolutionary Computation Conference;2024-07-14

4. Arbitrary style transformation algorithm based on multi-scale fusion and compressed attention in art and design;Intelligent Decision Technologies;2024-06-26

5. ADSynth: Synthesizing Realistic Active Directory Attack Graphs;2024 54th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN);2024-06-24

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