Detecting Cross-Site Scripting in Web Applications Using Fuzzy Inference System
With improvement in computing and technological advancements, web-based applications are now ubiquitous on the Internet. However, these web applications are becoming prone to vulnerabilities which have led to theft of confidential information, data loss, and denial of data access in the course of in...
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Journal of Computer Networks and Communications |
| Online Access: | http://dx.doi.org/10.1155/2018/8159548 |
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| _version_ | 1832549945152897024 |
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| author | Bakare K. Ayeni Junaidu B. Sahalu Kolawole R. Adeyanju |
| author_facet | Bakare K. Ayeni Junaidu B. Sahalu Kolawole R. Adeyanju |
| author_sort | Bakare K. Ayeni |
| collection | DOAJ |
| description | With improvement in computing and technological advancements, web-based applications are now ubiquitous on the Internet. However, these web applications are becoming prone to vulnerabilities which have led to theft of confidential information, data loss, and denial of data access in the course of information transmission. Cross-site scripting (XSS) is a form of web security attack which involves the injection of malicious codes into web applications from untrusted sources. Interestingly, recent research studies on the web application security centre focus on attack prevention and mechanisms for secure coding; recent methods for those attacks do not only generate high false positives but also have little considerations for the users who oftentimes are the victims of malicious attacks. Motivated by this problem, this paper describes an “intelligent” tool for detecting cross-site scripting flaws in web applications. This paper describes the method implemented based on fuzzy logic to detect classic XSS weaknesses and to provide some results on experimentations. Our detection framework recorded 15% improvement in accuracy and 0.01% reduction in the false-positive rate which is considerably lower than that found in the existing work by Koli et al. Our approach also serves as a decision-making tool for the users. |
| format | Article |
| id | doaj-art-7ea33c8bc2f842a98f37b6b2701e6027 |
| institution | Kabale University |
| issn | 2090-7141 2090-715X |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Computer Networks and Communications |
| spelling | doaj-art-7ea33c8bc2f842a98f37b6b2701e60272025-02-03T06:08:06ZengWileyJournal of Computer Networks and Communications2090-71412090-715X2018-01-01201810.1155/2018/81595488159548Detecting Cross-Site Scripting in Web Applications Using Fuzzy Inference SystemBakare K. Ayeni0Junaidu B. Sahalu1Kolawole R. Adeyanju2Department of Computer Science, Faculty of Sciences, Ahmadu Bello University, Zaria, NigeriaDepartment of Computer Science, Faculty of Sciences, Ahmadu Bello University, Zaria, NigeriaDepartment of Computer Science, Faculty of Sciences, Ahmadu Bello University, Zaria, NigeriaWith improvement in computing and technological advancements, web-based applications are now ubiquitous on the Internet. However, these web applications are becoming prone to vulnerabilities which have led to theft of confidential information, data loss, and denial of data access in the course of information transmission. Cross-site scripting (XSS) is a form of web security attack which involves the injection of malicious codes into web applications from untrusted sources. Interestingly, recent research studies on the web application security centre focus on attack prevention and mechanisms for secure coding; recent methods for those attacks do not only generate high false positives but also have little considerations for the users who oftentimes are the victims of malicious attacks. Motivated by this problem, this paper describes an “intelligent” tool for detecting cross-site scripting flaws in web applications. This paper describes the method implemented based on fuzzy logic to detect classic XSS weaknesses and to provide some results on experimentations. Our detection framework recorded 15% improvement in accuracy and 0.01% reduction in the false-positive rate which is considerably lower than that found in the existing work by Koli et al. Our approach also serves as a decision-making tool for the users.http://dx.doi.org/10.1155/2018/8159548 |
| spellingShingle | Bakare K. Ayeni Junaidu B. Sahalu Kolawole R. Adeyanju Detecting Cross-Site Scripting in Web Applications Using Fuzzy Inference System Journal of Computer Networks and Communications |
| title | Detecting Cross-Site Scripting in Web Applications Using Fuzzy Inference System |
| title_full | Detecting Cross-Site Scripting in Web Applications Using Fuzzy Inference System |
| title_fullStr | Detecting Cross-Site Scripting in Web Applications Using Fuzzy Inference System |
| title_full_unstemmed | Detecting Cross-Site Scripting in Web Applications Using Fuzzy Inference System |
| title_short | Detecting Cross-Site Scripting in Web Applications Using Fuzzy Inference System |
| title_sort | detecting cross site scripting in web applications using fuzzy inference system |
| url | http://dx.doi.org/10.1155/2018/8159548 |
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