Open Access

ARTICLE

Deploying Hybrid Ensemble Machine Learning Techniques for Effective Cross-Site Scripting (XSS) Attack Detection

by Noor Ullah Bacha¹, Songfeng Lu¹, Attiq Ur Rehman¹, Muhammad Idrees², Yazeed Yasin Ghadi³, Tahani Jaser Alahmadi^4,*

1 School of Cyber Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430073, China
2 Department of Computer Science and Engineering, University of Engineering and Technology, Lahore, 54000, Pakistan
3 Department of Computer Science and Software Engineering, Al Ain University, Al Ain, 12555, Abu Dhabi
4 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, 84428, Saudi Arabia

* Corresponding Author: Tahani Jaser Alahmadi. Email:

Computers, Materials & Continua 2024, 81(1), 707-748. https://doi.org/10.32604/cmc.2024.054780

Received 07 June 2024; Accepted 14 August 2024; Issue published 15 October 2024

Abstract

Cross-Site Scripting (XSS) remains a significant threat to web application security, exploiting vulnerabilities to hijack user sessions and steal sensitive data. Traditional detection methods often fail to keep pace with the evolving sophistication of cyber threats. This paper introduces a novel hybrid ensemble learning framework that leverages a combination of advanced machine learning algorithms—Logistic Regression (LR), Support Vector Machines (SVM), eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Deep Neural Networks (DNN). Utilizing the XSS-Attacks-2021 dataset, which comprises 460 instances across various real-world traffic-related scenarios, this framework significantly enhances XSS attack detection. Our approach, which includes rigorous feature engineering and model tuning, not only optimizes accuracy but also effectively minimizes false positives (FP) (0.13%) and false negatives (FN) (0.19%). This comprehensive methodology has been rigorously validated, achieving an unprecedented accuracy of 99.87%. The proposed system is scalable and efficient, capable of adapting to the increasing number of web applications and user demands without a decline in performance. It demonstrates exceptional real-time capabilities, with the ability to detect XSS attacks dynamically, maintaining high accuracy and low latency even under significant loads. Furthermore, despite the computational complexity introduced by the hybrid ensemble approach, strategic use of parallel processing and algorithm tuning ensures that the system remains scalable and performs robustly in real-time applications. Designed for easy integration with existing web security systems, our framework supports adaptable Application Programming Interfaces (APIs) and a modular design, facilitating seamless augmentation of current defenses. This innovation represents a significant advancement in cybersecurity, offering a scalable and effective solution for securing modern web applications against evolving threats.

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Keywords

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