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Title:Iskanje ranljivosti XSS v spletnih aplikacijah z uporabo metod strojnega učenja : magistrsko delo
Authors:ID Kozulić, Ivan (Author)
ID Lukač, Niko (Mentor) More about this mentor... New window
Files:.pdf MAG_Kozulic_Ivan_2020.pdf (1,24 MB)
MD5: 71526EE12E0241A652EE9084B5A344B8
PID: 20.500.12556/dkum/ca477e1b-5df1-4dd0-946c-fd324ca6a5b7
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:Cross-site scripting (XSS) napadi še vedno predstavljajo veliko varnostno tveganje pri spletnih aplikacijah. V magistrskem delu predstavljamo metodo za iskanje ranljivosti v JavaScript programski kodi, pri čemer smo uporabili algoritme strojnega učenja. V teoretičnem delu najprej opišemo osnovne koncepte napadov XSS in z njimi povezane ranljivosti. Predstavimo tudi sorodne pristope za iskanje ranljivosti XSS. V praktičnem delu magistrskega dela pa se posvetimo načinu izračuna značilnic iz JavaScript kode ter pripravi učne in testne množice. Na podlagi značilnic smo usposobili model strojnega učenja za ločevanje ranljivih od neranljivih aplikacij. Iz rezultatov sklepamo, da je metoda učinkovita in nudi dodatno podporo pri odkrivanju ranljivosti XSS.
Keywords:varnost spletnih aplikacij, XSS, JavaScript, strojno učenje
Place of publishing:Maribor
Place of performance:Maribor
Publisher:[I. Kozulić]
Year of publishing:2020
Number of pages:IX, 60 f.
PID:20.500.12556/DKUM-76839 New window
UDC:004.774.056+004.85(043.2)
COBISS.SI-ID:37733123 New window
NUK URN:URN:SI:UM:DK:16MHOYPH
Publication date in DKUM:04.11.2020
Views:1231
Downloads:111
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
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Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:14.07.2020

Secondary language

Language:English
Title:Xss vulnerabilities search in web applications by using machine learning approaches
Abstract:Cross-site scripting (XSS) attacks are still a major threat to the security of web applications. In the master's thesis, we present a method for searching vulnerabilities in JavaScript code using machine learning algorithms. In the theoretical part, we first describe the basic concepts of XSS attacks and related vulnerabilities. We also present related approaches for finding XSS vulnerabilities. In the practical part of the thesis, we focus on the method for calculating the characteristics from the JavaScript code and the preparation of the train and test data set. Based on the characteristics, we trained the machine learning model to separate vulnerable from non-vulnerable applications. From the results, we conclude that the method is effective and offers additional support in detecting vulnerabilities.
Keywords:web application security, XSS, JavaScript, machine learning


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