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Prediktion av sannolikhet med maskininlärning och pris för sårbarheter i programvaruprodukter: Prototyper med algoritmer för prediktion av sårbarheter
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics.
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics.
2018 (Swedish)Independent thesis Basic level (university diploma), 10 credits / 15 HE creditsStudent thesisAlternative title
Prediction of probability with machine learning and price for vulnerabilities in software products : Prototypes with algorithms for prediction of vulnerabilities (English)
Abstract [sv]

Arbetet undersöker möjligheterna att prediktera sårbarheter för programvaruprodukter och vilken typ av sårbarhet som kommer hittas. Det är en indikation på hur utsatt ett system är med hur ofta en sårbarhet hittas. Maskininlärning är ett sätt att kunna utläsa mönster ur en stor mängd data och kan hjälpa till med prioritering. Med publikt tillgänglig data om sårbarheter har maskininlärning använts för att hitta en metod att göra prediktioner om antalet kommande sårbarheter. Fem olika prototyper har tagits fram och granskats om vilken prototyp som presterar bäst i testerna. En analys av resultaten visar att innehållet i datan borde vara jämnt fördelat mellan klasser i en sådan här typ av undersökning för att få ett bra och pålitligt resultat.

Antalet publika sårbarheter som hittas och publiceras har ökat de senaste åren och ser ut att fortsätta i samma trend. En analys på hur marknaden och försäljningspriset ser ut för sårbarheter visar att den globala ekonomin ökar i stor takt och omsättningen för maskininlärning kommer att öka kraftigt då man inser dess fördelar inom fler områden.

Abstract [en]

The project explores the possibilities of predicting software product vulnerabilities and the type of vulnerability that will be found. It is an indication of how vulnerable a system is on how often a vulnerability is found. Machine learning is a way to read patterns from a large amount of data and can help with prioritization. With publicly available data on vulnerabilities, machine learning has been used to find a method of making predictions about the number of future vulnerabilities.

prototypes have been developed and reviewed for which prototype that performs

best. An analysis of the results shows that the content of the data should be evenly

divided between classes in such type of survey to get a good and reliable result.

The number of public vulnerabilities found and published has increased in recent

years and appears to continue in the same trend. An analysis of how the market and

selling price are looking for vulnerabilities shows that the global economy is growing

rapidly and the turnover for machine learning will increase significantly as it

recognizes its benefits to more areas.

Place, publisher, year, edition, pages
2018. , p. 56
Series
TRITA-CBH-GRU ; 2018:6
Keywords [en]
Machine learning, vulnerabilities, algorithms, neural networks, vulnerability economy
Keywords [sv]
Maskininlärning, sårbarheter, algoritmer, neurala nätverk, sårbarhetsekonomi
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-223371OAI: oai:DiVA.org:kth-223371DiVA, id: diva2:1184285
Subject / course
Computer Engineering with Business Economics
Educational program
Bachelor of Science in Engineering - Engineering and Economics
Supervisors
Examiners
Available from: 2018-02-23 Created: 2018-02-20 Last updated: 2018-02-23Bibliographically approved

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