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**Harvard**

Ramström, H. och Backman, J. (2018) *Efficient Solving Methods for POMDP-based Threat Defense Environments on Bayesian Attack Graphs*. Göteborg : Chalmers University of Technology

** BibTeX **

@mastersthesis{

Ramström2018,

author={Ramström, Hampus and Backman, Johan},

title={Efficient Solving Methods for POMDP-based Threat Defense Environments on Bayesian Attack Graphs},

abstract={In this work, we show how to formulate a threat defense environment as a Partially
Observable Markov Decision Process (POMDP) that allows for fast approximate
defense algorithms against multiple attackers. It is done through an action extension,
coined the Inspect action, which allows the agent to reveal the true state of
the environment, thereby reducing the problem into a traditional Markov Decision
Process (MDP) for the current time-step. The work is an extension of previous definitions
of the same problem. Furthermore, based on the new definition we define
and show the optimal policy, as well as two new solving algorithms, n-Myopic and
n-Lookahead. To evaluate their performance, we show and compare the results of
these new algorithms to more standard solving algorithms, such as Q-learning and
Policy Gradients.
The experimental results show that the new algorithms perform better than previous
attempts and allows for larger scale threat environments thanks to the approximate
MDP reduction. Additionally, to facilitate future research, two OpenAI Gym environments
were developed and are publicly available for new research to build upon.
We encourage new research with similar problem description to use this software
library, opening up to standardized performance results.},

publisher={Institutionen för data- och informationsteknik (Chalmers), Chalmers tekniska högskola},

place={Göteborg},

year={2018},

keywords={Reinforcement Learning, POMDP, Bayesian Attack Graphs, Security, Defense Policies, OpenAI Gym, Threat Defense},

note={82},

}

** RefWorks **

RT Generic

SR Electronic

ID 256400

A1 Ramström, Hampus

A1 Backman, Johan

T1 Efficient Solving Methods for POMDP-based Threat Defense Environments on Bayesian Attack Graphs

YR 2018

AB In this work, we show how to formulate a threat defense environment as a Partially
Observable Markov Decision Process (POMDP) that allows for fast approximate
defense algorithms against multiple attackers. It is done through an action extension,
coined the Inspect action, which allows the agent to reveal the true state of
the environment, thereby reducing the problem into a traditional Markov Decision
Process (MDP) for the current time-step. The work is an extension of previous definitions
of the same problem. Furthermore, based on the new definition we define
and show the optimal policy, as well as two new solving algorithms, n-Myopic and
n-Lookahead. To evaluate their performance, we show and compare the results of
these new algorithms to more standard solving algorithms, such as Q-learning and
Policy Gradients.
The experimental results show that the new algorithms perform better than previous
attempts and allows for larger scale threat environments thanks to the approximate
MDP reduction. Additionally, to facilitate future research, two OpenAI Gym environments
were developed and are publicly available for new research to build upon.
We encourage new research with similar problem description to use this software
library, opening up to standardized performance results.

PB Institutionen för data- och informationsteknik (Chalmers), Chalmers tekniska högskola,PB Institutionen för data- och informationsteknik (Chalmers), Chalmers tekniska högskola,

LA eng

LK http://publications.lib.chalmers.se/records/fulltext/256400/256400.pdf

OL 30