Ensemble Methods to Detect XSS Attacks

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.

@article{Nagarjun2020,
  title     = {Ensemble Methods to Detect XSS Attacks},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {11},
  number    = {5},
  year      = {2020},
  publisher = {The Science and Information Organization},
  author    = {PMD Nagarjun and Shaik Shakeel Ahamad},
  doi       = {10.14569/IJACSA.2020.0110585},
  url       = {https://doi.org/10.14569/IJACSA.2020.0110585}
}