IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v16y2024i12p482-d1550713.html
   My bibliography  Save this article

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

Author

Listed:
  • Mahmoud AlJamal

    (Department of Cybersecurity, Science and Information Technology, Irbid National University, Irbid 21110, Jordan
    Department of Information Technology, Faculty of Prince Al-Hussien bin Abdullah, Hashemite University, Zarqa 13133, 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)

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.

Suggested Citation

  • Mahmoud AlJamal & Rabee Alquran & Ayoub Alsarhan & Mohammad Aljaidi & Mohammad Alhmmad & Wafa’ Q. Al-Jamal & Nasser Albalawi, 2024. "A Robust Machine Learning Model for Detecting XSS Attacks on IoT over 5G Networks," Future Internet, MDPI, vol. 16(12), pages 1-18, December.
    • Handle: RePEc:gam:jftint:v:16:y:2024:i:12:p:482-:d:1550713
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/16/12/482/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1999-5903/16/12/482/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Muhammad Bin Saif & Sara Migliorini & Fausto Spoto, 2024. "A Survey on Data Availability in Layer 2 Blockchain Rollups: Open Challenges and Future Improvements," Future Internet, MDPI, vol. 16(9), pages 1-16, August.
    2. Waleed Shahjehan & Rajkumar Singh Rathore & Syed Waqar Shah & Mohammad Aljaidi & Ali Safaa Sadiq & Omprakash Kaiwartya, 2024. "A Review on Millimeter-Wave Hybrid Beamforming for Wireless Intelligent Transport Systems," Future Internet, MDPI, vol. 16(9), pages 1-40, September.
    3. Michele De Donno & Alberto Giaretta & Nicola Dragoni & Antonio Bucchiarone & Manuel Mazzara, 2019. "Cyber-Storms Come from Clouds: Security of Cloud Computing in the IoT Era," Future Internet, MDPI, vol. 11(6), pages 1-30, June.
    4. Md Motiur Rahman & Deepti Gupta & Smriti Bhatt & Shiva Shokouhmand & Miad Faezipour, 2024. "A Comprehensive Review of Machine Learning Approaches for Anomaly Detection in Smart Homes: Experimental Analysis and Future Directions," Future Internet, MDPI, vol. 16(4), pages 1-20, April.
    5. Bing Su & Jiwu Liang, 2024. "Research on Secure Community Opportunity Network Based on Trust Model," Future Internet, MDPI, vol. 16(4), pages 1-22, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Myung-Sun Baek & Hyoung-Kyu Song, 2024. "Decision Feedback Equalization-Based Low-Complexity Interference Cancellation and Signal Detection Technique Based for Non-Orthogonal Signaling," Mathematics, MDPI, vol. 12(23), pages 1-16, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jftint:v:16:y:2024:i:12:p:482-:d:1550713. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.