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Attack Graph Analysis Based on Marko...

Li, Ming.

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Attack Graph Analysis Based on Markov Decision Process.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Attack Graph Analysis Based on Markov Decision Process./
Author:	Li, Ming.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
Description:	93 p.
Notes:	Source: Masters Abstracts International, Volume: 56-04.
Contained By:	Masters Abstracts International56-04(E).
Subject:	Electrical engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10270833
ISBN:	9781369724912

Attack Graph Analysis Based on Markov Decision Process.
Li, Ming.

Attack Graph Analysis Based on Markov Decision Process. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 93 p.

Source: Masters Abstracts International, Volume: 56-04.

Thesis (M.Eng.)--The University of Tulsa, 2017.

An Attack Graph (AG) is an abstract representation of all the existing paths attackers can take to compromise a network. The quantitative analysis of an AG can be based on using probabilities, and the analytical results can indicate the paths that attackers are more likely to choose and the hosts in the network that are highly valuable goals for attackers. Markov Decision Processes (MDP) are one of the probability based analyses. By introducing rewards and alternatives, an AG can be fully described by a MDP. This thesis uses insertion of states to implement the conversion from an AG into a MDP, and then uses value iteration and policy iteration methods to conduct reward analysis on MDPs. The output of reward analysis is the Maximum Total Expected Reward (MTER) and the Optimal Policy Vector (OPV). For the analysis of an AG, the OPV can indicate specific transitions on an attacking path that may bring the MTER to attackers. Security engineers can use it to find potential security holes in the network and take preventive measures.

ISBN: 9781369724912Subjects--Topical Terms:

649834
Electrical engineering.

Attack Graph Analysis Based on Markov Decision Process.
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260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2017
300 $a 93 p.
500 $a Source: Masters Abstracts International, Volume: 56-04.
500 $a Adviser: Peter J. Hawrylak.
502 $a Thesis (M.Eng.)--The University of Tulsa, 2017.
520 $a An Attack Graph (AG) is an abstract representation of all the existing paths attackers can take to compromise a network. The quantitative analysis of an AG can be based on using probabilities, and the analytical results can indicate the paths that attackers are more likely to choose and the hosts in the network that are highly valuable goals for attackers. Markov Decision Processes (MDP) are one of the probability based analyses. By introducing rewards and alternatives, an AG can be fully described by a MDP. This thesis uses insertion of states to implement the conversion from an AG into a MDP, and then uses value iteration and policy iteration methods to conduct reward analysis on MDPs. The output of reward analysis is the Maximum Total Expected Reward (MTER) and the Optimal Policy Vector (OPV). For the analysis of an AG, the OPV can indicate specific transitions on an attacking path that may bring the MTER to attackers. Security engineers can use it to find potential security holes in the network and take preventive measures.
520 $a In the implementation of reward analysis, this thesis uses a hybrid programming model which combines Message Passing Interface (MPI) and Open Computing Language (OpenCL) on a heterogeneous computing cluster. The performance test conducted on the high performance computing cluster indicates that the parallel program based on the model can efficiently solve MDP with up to 1,000,000 states, which satisfies the demands to analyze today's large-scale AGs.
590 $a School code: 0236.
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