Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/139582
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Type: Conference paper
Title: A Scalable Double Oracle Algorithm for Hardening Large Active Directory Systems
Author: Zhang, Y.
Ward, M.
Guo, M.
Nguyen, H.
Citation: Proceedings of the ACM Conference on Computer and Communications Security, 2023, pp.993-1003
Publisher: Association for Computing Machinery
Publisher Place: Online
Issue Date: 2023
ISBN: 9798400700989
ISSN: 1543-7221
Conference Name: ACM ASIA Conference on Computer and Communications Security (ACM ASIACCS) (10 Jul 2023 - 14 Jul 2023 : Melbourne Victoria, Australia)
Statement of
Responsibility: 
Yumeng Zhang, Max Ward, Mingyu Guo, Hung Nguyen
Abstract: Active Directory (AD) is a popular information security management system for Windows domain networks and is an ongoing common target for cyber attacks. Most real-world Active Directory systems consist of millions of entities and links, and there are currently no efficient and effective solutions for hardening Active Directory systems of such scale. In this paper, we propose a novel and scalable double oracle-based algorithm for hardening large AD systems. We formulate the problem as a Stackelberg game between the defender and the attacker on a weighted AD attack graph, where the defender acts as the leader with a budget, and the objective is to find an optimal defender’s pure strategy. We show that our double oracle-based solution has significantly improved speed and scalability compared with previous solutions for hardening AD systems. Lastly, we compare with GoodHound weakest links and show that our solution provides better recommendations for targeting the elimination of optimal attack paths.
Keywords: Active Directory; Network security; Attack graph; Stackelberg game; Double oracle
Rights: © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.
DOI: 10.1145/3579856.3590343
Grant ID: http://purl.org/au-research/grants/arc/NI210100139
Published version: https://dl.acm.org/doi/proceedings/10.1145/3579856
Appears in Collections:Computer Science publications

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