Abstract
Machine learning techniques are gaining popularity and giving better results in detecting Web application attacks. Cross-site scripting is an injection attack widespread in web applications. The existing solutions like filter-based, dynamic analysis, and static analysis are not effective in detecting unknown XSS attacks, and machine learning methods can detect unknown XSS attacks. Existing research to detect XSS attacks by using machine learning methods have issues like single base classifiers, small datasets, and unbalanced datasets. In this paper, supervised ensemble learning techniques trained on a large labeled and balanced dataset to detect XSS attacks. The ensemble methods used in this research are random forest classification, AdaBoost, bagging with SVM, Extra-Trees, gradient boosting, and histogram-based gradient boosting. Analyzed and compared the performance of ensemble learning algorithms by using the confusion matrix.
Highlights
Machine learning algorithms are useful in detecting unknown and new XSS attacks in Web Applications
AdaBoost, bagging with SVM, ExtraTrees, gradient boosting, random forest classification, and histogram-based gradient boosting models are trained on a large labeled dataset and evaluated these methods performance based on their accuracy, recall, precision, and the F-measure
We developed and analyzed supervised ensemble machine learning methods to detect XSS attacks in Web applications
Summary
Machine learning algorithms are useful in detecting unknown and new XSS attacks in Web Applications. Ensemble methods are a combination of different base models, and the ensemble learning models can give optimal results compared to base models [1]. In XSS attacks, the attacker can steal victim’s session cookie, sensitive data of victim, implement keyloggers at browser, and damage the reputation of a trusted Website. A common problem in existing XSS prevention techniques are the incapability of detecting unknown or new XSS attacks [2]. Effective XSS detection models can be built by using ensemble learning techniques. AdaBoost, bagging, ExtraTrees, gradient boosting, random forest, histogram-based gradient boosting are ensemble methods, which uses base models like decision trees, etc
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Advanced Computer Science and Applications
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
