A Robust Machine Learning Model for Detecting XSS Attacks on IoT over 5G Networks

Mahmoud AlJamal, Rabee Alquran, Ayoub Alsarhan, Mohammad Aljaidi (), Mohammad Alhmmad, Wafa’ Q. Al-Jamal and Nasser Albalawi
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Mahmoud AlJamal: Department of Cybersecurity, Science and Information Technology, Irbid National University, Irbid 21110, Jordan
Rabee Alquran: Department of Information Technology, Faculty of Prince Al-Hussien bin Abdullah, Hashemite University, Zarqa 13133, Jordan
Ayoub Alsarhan: Department of Information Technology, Faculty of Prince Al-Hussien bin Abdullah, Hashemite University, Zarqa 13133, Jordan
Mohammad Aljaidi: Department of Computer Science, Faculty of Information Technology, Zarqa University, Zarqa 13110, Jordan
Mohammad Alhmmad: Department of Information Technology, Faculty of Prince Al-Hussien bin Abdullah, Hashemite University, Zarqa 13133, Jordan
Wafa’ Q. Al-Jamal: Faculty of Science and Technology (FST), Universiti Sains Islam Malaysia (USIM), Nilai 71800, Malaysia
Nasser Albalawi: Department of Computer Science, Faculty of Computing and Information Technology, Northern Border University, Rafha 91911, Saudi Arabia

Future Internet, 2024, vol. 16, issue 12, 1-18

Abstract: As the Internet of Things (IoT) expands rapidly and 5G networks become more widespread, the need for strong cybersecurity measures in IoT systems has become increasingly critical. Traditional security methods are no longer sufficient due to the shear volume, diversity, and limited processing capabilities of IoT devices. The high data transmission speeds of 5G networks further intensify the potential risks, making it essential to implement effective security measures. One of the major threats to IoT systems is Cross-Site Scripting (XSS) attacks. To address this issue, we introduce a new machine learning (ML) approach designed to detect and predict XSS attacks on IoT systems operating over 5G networks. By using ML classifiers, particularly the Random Forest classifier, our approach achieves a high classification accuracy of 99.89% in identifying XSS attacks. This research enhances IoT security by addressing the emerging challenges posed by 5G networks and XSS attacks, ensuring the safe operation of IoT devices within the 5G ecosystem through early detection and prevention of vulnerabilities.

Keywords: Internet of Things (IoT); 5G networks; XSS attacks; machine learning (ML); cybersecurity (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
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