Combining online learning and equilibrium computation in security games

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F15%3A00237769" target="_blank" >RIV/68407700:21230/15:00237769 - isvavai.cz</a>

Výsledek na webu

<a href="http://link.springer.com/chapter/10.1007/978-3-319-25594-1_8" target="_blank" >http://link.springer.com/chapter/10.1007/978-3-319-25594-1_8</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-25594-1_8" target="_blank" >10.1007/978-3-319-25594-1_8</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Combining online learning and equilibrium computation in security games

Popis výsledku v původním jazyce

Game-theoretic analysis has emerged as an important method for making resource allocation decisions in both infrastructure protection and cyber security domains. However, static equilibrium models defined based on inputs from domain experts have weaknesses; they can be inaccurate, and they do not adapt over time as the situation (and adversary) evolves. In cases where there are frequent interactions with an attacker, using learning to adapt to an adversary revealed behavior may lead to better solutionsin the long run. However, learning approaches need a lot of data, may perform poorly at the start, and may not be able to take advantage of expert analysis. We explore ways to combine equilibrium analysis with online learning methods with the goal of gaining the advantages of both approaches. We present several hybrid methods that combine these techniques in different ways, and empirically evaluated the performance of these methods in a game that models a border patrolling scenario.

Název v anglickém jazyce

Combining online learning and equilibrium computation in security games

Popis výsledku anglicky

Game-theoretic analysis has emerged as an important method for making resource allocation decisions in both infrastructure protection and cyber security domains. However, static equilibrium models defined based on inputs from domain experts have weaknesses; they can be inaccurate, and they do not adapt over time as the situation (and adversary) evolves. In cases where there are frequent interactions with an attacker, using learning to adapt to an adversary revealed behavior may lead to better solutionsin the long run. However, learning approaches need a lot of data, may perform poorly at the start, and may not be able to take advantage of expert analysis. We explore ways to combine equilibrium analysis with online learning methods with the goal of gaining the advantages of both approaches. We present several hybrid methods that combine these techniques in different ways, and empirically evaluated the performance of these methods in a game that models a border patrolling scenario.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

—

Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Decision and Game Theory for Security

ISBN

978-3-319-25593-4

ISSN

0302-9743

e-ISSN

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Počet stran výsledku

20

Strana od-do

130-149

Název nakladatele

Springer

Místo vydání

London

Místo konání akce

London

Datum konání akce

4. 11. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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