Multi-Stage Attack Graph Security Games: Heuristic Strategies, with Empirical Game-Theoretic Analysis --- Dataset

Nguyen, Thanh H.; Wright, Mason; Wellman, Michael P.; Singh, Satinder

Work Description

Title: Multi-Stage Attack Graph Security Games: Heuristic Strategies, with Empirical Game-Theoretic Analysis --- Dataset Open Access Deposited

Attribute	Value
Methodology	Empirical Game-Theoretic Analysis Online
Description	In this work , we study the problem of allocating limited security countermeasures to protect network data from cyber-attacks, for scenarios modeled by Bayesian attack graphs. We consider multi-stage interactions between a network administrator and cybercriminals, formulated as a security game. We propose parameterized heuristic strategies for the attacker and defender and provide detailed analysis of their time complexity. Our heuristics exploit the topological structure of attack graphs and employ sampling methods to overcome the computational complexity in predicting opponent actions. Due to the complexity of the game, we employ a simulation-based approach and perform empirical game analysis over an enumerated set of heuristic strategies. Finally, we conduct experiments in various game settings to evaluate the performance of our heuristics in defending networks, in a manner that is robust to uncertainty about the security environment.
Creator	Nguyen, Thanh H. Wright, Mason Wellman, Michael P. Singh, Satinder
Depositor	thanhhng@umich.edu
Contact information	thanhhng@umich.edu
Discipline	Science Engineering
Funding agency	Department of Defense (DOD)
Other Funding agency	Department of Defense (DOD)
ORSP grant number	W911NF-13-1-0421
Keyword	Empirical Game-Theoretic Analysis Multi-stage Security Games Attack Graph Game Theory Moving Target Defense
Citations to related material	Nguyen, T. H., Wright, M., Wellman, M. P., & Singh, S. (2017). Multi-stage attack graph security games: Heuristic strategies, with empirical game-theoretic analysis. In MTD 2017 - Proceedings of the 2017 Workshop on Moving Target Defense, co-located with CCS 2017 (Vol. 2017-January, pp. 87-97). Association for Computing Machinery, Inc. https://doi.org/10.1145/3140549.3140562
Resource type	Dataset
Last modified	07/02/2018
DOI	doi:10.7302/Z2F18WX2
CC License	http://creativecommons.org/licenses/by-nc/4.0/

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