fmereani / Cross-Site-Scripting-XSS

This project contains datasets for Cross Site Scripting (XSS), SQL, and LDAP injections. The project also contains the Matlab code for creating SVM, K-NN, Random Forest, and Neural Networks classifiers to detect Web applications attacks.

Cross-Site-Scripting

This project contains dataset for Cross Site Scripting(XSS). The project contains the Matlab code for creating SVM, K-NN, Random Forest, and Neural Networks classifiers to detect Web applications attacks.

Citations

If f you would like to cite the datasets or code, please use the following references:

1. Mereani, F. A. and Howe, J. M. (2018). Detecting Cross-Site Scripting Attacks Using Machine Learning. In Advanced Machine Learning Technologies and Applications, volume 723 of AISC, pages 200–210. Springer.
   Link: https://link.springer.com/chapter/10.1007/978-3-319-74690-6_20

2. Mereani, F. A. and Howe, J. M. (2018). Preventing Cross-Site Scripting Attacks by Combining Classifiers. In Proceedings of the 10th International Joint Conference on Computational Intelligence - Volume 1, pages 135–143. SciTePress.
   Link: https://www.scitepress.org/Link.aspx?doi=10.5220/0006894901350143

3. Mereani., F. A. and Howe., J. M. (2019). Exact and approximate rule extraction from neural networks with boolean features. In Proceedings of the 11th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2019), pages 424–433. INSTICC, SciTePress.T
   Link: https://www.scitepress.org/Link.aspx?doi=10.5220/0008362904240433