Academic Journal
Malicious XSS Code Detection with Decision Tree.
| Τίτλος: | Malicious XSS Code Detection with Decision Tree. |
|---|---|
| Συγγραφείς: | KASIM, Ömer |
| Πηγή: | Journal of Polytechnic; Mar2020, Vol. 23 Issue 1, p67-72, 7p |
| Θεματικοί όροι: | Electronic commerce, HTML (Document markup language), Quantitative research, Mobile apps, Feature extraction, Decision trees |
| Περίληψη: | Dynamic applications such as e-commerce, blogs, forums, e-governance, e-banking and portals that are in these platforms have become a part of our lives. However, a tremendous increase in the use of dynamic web and mobile applications has resulted in security vulnerabilities originating from the Hypertext Markup Language (HTML) coding system. Site-to-site Script Execution (XSS) attack is the largest contributors to security exploits. There are different models according to the dynamic content that XSS attacks use. The interest of the study is composed of attacks on visual content with the "img" tag. In study, an algorithm has been developed to detect XSS attacks with the decision tree which is motivated by the fact that they tend to be easier to implement and interpret than other quantitative data-driven methods. The algorithm that successfully classifies 392 of 400 malicious and clean codes in the data set with 8 different features. This result contributes to the use of secure internet without XSS attacks that use visual content.. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Polytechnic is the property of Journal of Polytechnic and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Text: Availability: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Malicious XSS Code Detection with Decision Tree. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22KASIM%2C+Ömer%22">KASIM, Ömer</searchLink> – Name: TitleSource Label: Source Group: Src Data: Journal of Polytechnic; Mar2020, Vol. 23 Issue 1, p67-72, 7p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Electronic+commerce%22">Electronic commerce</searchLink><br /><searchLink fieldCode="DE" term="%22HTML+%28Document+markup+language%29%22">HTML (Document markup language)</searchLink><br /><searchLink fieldCode="DE" term="%22Quantitative+research%22">Quantitative research</searchLink><br /><searchLink fieldCode="DE" term="%22Mobile+apps%22">Mobile apps</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+trees%22">Decision trees</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Dynamic applications such as e-commerce, blogs, forums, e-governance, e-banking and portals that are in these platforms have become a part of our lives. However, a tremendous increase in the use of dynamic web and mobile applications has resulted in security vulnerabilities originating from the Hypertext Markup Language (HTML) coding system. Site-to-site Script Execution (XSS) attack is the largest contributors to security exploits. There are different models according to the dynamic content that XSS attacks use. The interest of the study is composed of attacks on visual content with the "img" tag. In study, an algorithm has been developed to detect XSS attacks with the decision tree which is motivated by the fact that they tend to be easier to implement and interpret than other quantitative data-driven methods. The algorithm that successfully classifies 392 of 400 malicious and clean codes in the data set with 8 different features. This result contributes to the use of secure internet without XSS attacks that use visual content.. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Journal of Polytechnic is the property of Journal of Polytechnic and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edb&AN=141241523 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.2339/politeknik.470332 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 7 StartPage: 67 Subjects: – SubjectFull: Electronic commerce Type: general – SubjectFull: HTML (Document markup language) Type: general – SubjectFull: Quantitative research Type: general – SubjectFull: Mobile apps Type: general – SubjectFull: Feature extraction Type: general – SubjectFull: Decision trees Type: general Titles: – TitleFull: Malicious XSS Code Detection with Decision Tree. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: KASIM, Ömer IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2020 Type: published Y: 2020 Identifiers: – Type: issn-print Value: 13020900 Numbering: – Type: volume Value: 23 – Type: issue Value: 1 Titles: – TitleFull: Journal of Polytechnic Type: main |
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