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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Bibliographic Details
Published inJournal of Computer Networks and Communications Vol. 2018; no. 2018; pp. 1 - 10
Main Authors Ayeni, Bakare K., Adeyanju, Kolawole R., Sahalu, Junaidu B.
Format Journal Article
LanguageEnglish
Published Cairo, Egypt Hindawi Publishing Corporation 01.01.2018
Hindawi
Hindawi Limited
Wiley
Subjects
Applications programs
Computers
Cybersecurity
Data loss
Decision making
Engineering
Flaw detection
Fuzzy logic
Fuzzy sets
Fuzzy systems
International conferences
Internet
Internet security
Internet software
Malware
Scanners
Software reliability
Structured Query Language-SQL
Technology application
Theft
Vulnerability
Web applications
Web sites (World Wide Web)
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Summary: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.
ISSN:2090-7141
2090-715X
DOI:10.1155/2018/8159548