The web applications cross site scripting attacks and preventions using machine learning technique

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dc.contributor.authorAlyasin, Eman Ibrahim
dc.contributor.authorAta, Oğuz
dc.contributor.authorÖzturk, Bilal A.
dc.date.accessioned2024-10-23T06:57:36Z
dc.date.available2024-10-23T06:57:36Z
dc.date.issued2024en_US
dc.departmentEnstitüler, Lisansüstü Eğitim Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalıen_US
dc.description.abstractWeb applications are utilized everywhere these days to share services and data online. Because companies deal with sensitive data, hackers have found them attractive targets. Vulnerabilities persist despite the numerous security procedures we've created to safeguard these applications. Major security issues have been identified in web applications used by various organizations, such as banks, healthcare providers, finance companies, and retail businesses. Cross-site scripting (XSS) attacks are one of the most significant issues, according to a report from White Hat Security. These attacks enable hackers to execute harmful programs on a user's web browser, resulting in issues such as the theft of data, cookies, passwords, and credit card numbers. This study focuses on the primary weaknesses present in contemporary web applications, particularly XSS attacks. We go over the many kinds of XSS attacks, provide instances from the real world, and describe how they operate. We also examine defenses against these attacks, discussing what works and what doesn't.en_US
dc.identifier.citationAlyasin, E. I., Ata, O., Öztürk, B. A. (2024). The web applications cross site scripting attacks and preventions using machine learning technique. International Journal of Multiphysics, 18(3), 1116-1120.en_US
dc.identifier.endpage1120en_US
dc.identifier.issn1750-9548
dc.identifier.issue3en_US
dc.identifier.startpage1116en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12939/4944
dc.identifier.volume18en_US
dc.institutionauthorAlyasin, Eman Ibrahim
dc.institutionauthorAta, Oğuz
dc.language.isoen
dc.relation.ispartofInternational Journal of Multiphysics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - İdari Personel ve Öğrencien_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCross Site Scriptingen_US
dc.subjectWeb attacksen_US
dc.subjectMachine learning XSS Mitigationsen_US
dc.titleThe web applications cross site scripting attacks and preventions using machine learning technique
dc.typeArticle

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