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Skydd av Kritisk Infrastruktur med Machine Learning
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering.
2019 (Swedish)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [sv]

Syftet med detta arbete är att utvärdera om Nash-strategi kan vara ett alternativ till Stackelberg för skydd av tågstationer, med hjälp av Machine Learning och spelteori. Detta arbete beskriver utvecklingen och testning av två algoritmer i en simulerad miljö, schack. Schack fungerade som bra testmiljö, för att få konkreta resultat och poängsättning. Samtidigt fick undertecknad bekräftelse att Nash-strategin fungerade på spel där all information erhålls.     

Dessa resultat och den bäst presterande algoritmen användes för att bestämma på vilka stationer man bör placera NBK-sensorer, som skyddar mot nukleära, biologiska och kemiska attacker. Resultaten av studien visade att Nash-strategi med hjälp av Minimax-algoritmen är ett hållbart alternativ till Stackelberg inom både säkerhetsdomänen, men också utanför. Slutsatsen som drogs var att Nash har god potential för framtida studier och att ytterligare studier bör genomföras för att undersöka fler variabler och effekterna av användning av Nash istället för Stackelberg. 

Abstract [en]

The purpose of this paper is to evaluate if the Nash-strategy could be an alternative to Stackelberg for protection of subway stations, with the help of Machine Learning and Game Theory. This thesis describes the development and testing of two algorithms in a simulated environment, chess. Chess worked as a test environment, to get accurate results and scoring. At the same time the author got confirmation that the Nashstrategy worked for games were all information is available.     

These results and the best performing algorithm were used to decide on which stations NBK-sensors should be placed, which protects against nuclear, biological and chemical attacks. The results of the study showed that the Nash-strategy with the help of the Minimax algorithm is a viable option to Stackelberg in the security domain, but also outside the security domain. The conclusions that were made is that Nash has good potential for future studies and should be examined further with more variables and the effects of using Nash instead of Stackelberg in security games

Place, publisher, year, edition, pages
2019.
National Category
Information Systems
Identifiers
URN: urn:nbn:se:ltu:diva-74514OAI: oai:DiVA.org:ltu-74514DiVA, id: diva2:1324519
Educational program
Systems Sciences, bachelor's level
Supervisors
Examiners
Available from: 2019-06-20 Created: 2019-06-13 Last updated: 2019-06-20Bibliographically approved

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