The web applications cross site scripting attacks and preventions using machine learning technique
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Tarih
2024
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Web 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.
Açıklama
Anahtar Kelimeler
Cross Site Scripting, Web attacks, Machine learning XSS Mitigations
Kaynak
International Journal of Multiphysics
WoS Q Değeri
Scopus Q Değeri
Cilt
18
Sayı
3
Künye
Alyasin, 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.
