XSS Attack Detection with N-Gram Based Prediction Model

Authors : Bilal ALAGHA

Pages : 1-9

Doi:10.53608/estudambilisim.1233344

View : 87 | Download : 84

Publication Date : 2023-06-30

Article Type : Research Paper

Abstract :The increment developments in technology has empowered the web applications. Meanwhile, the existence of Cross-Site Scripting insert ignore into journalissuearticles values(XSS); vulnerabilities in web applications has become a concern for users. In spite of the numerous current detection approaches, attackers have been exploiting XSS vulnerabilities for years, causing harm to the internet users. In this paper, a text-mining based approach to detect XSS attacks in web applications is introduced. This approach is built to extract a set of features from a publicly available source code files, which are then used to build a prediction model. The findings include few comparisons between Word Tokenization and N-Gram in accuracy, time spend to build the model and AUC-ROC curve. The results show that N-Gram tokenization outperforms the Word Tokenization.
Keywords : XSS Saldırıları, Tahmin Modeli, Makine Öğrenme, Kelime Simgeleştirme, N Gram, Algoritma