A Robust Machine Learning Model for Detecting XSS Attacks on IoT over 5G Networks
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 capabil...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Future Internet |
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| Online Access: | https://www.mdpi.com/1999-5903/16/12/482 |
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| author | Mahmoud AlJamal Rabee Alquran Ayoub Alsarhan Mohammad Aljaidi Mohammad Alhmmad Wafa’ Q. Al-Jamal Nasser Albalawi |
| author_facet | Mahmoud AlJamal Rabee Alquran Ayoub Alsarhan Mohammad Aljaidi Mohammad Alhmmad Wafa’ Q. Al-Jamal Nasser Albalawi |
| author_sort | Mahmoud AlJamal |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-dc99cce936ef4e51acc784a47bb2554c |
| institution | Kabale University |
| issn | 1999-5903 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj-art-dc99cce936ef4e51acc784a47bb2554c2024-12-27T14:27:25ZengMDPI AGFuture Internet1999-59032024-12-01161248210.3390/fi16120482A Robust Machine Learning Model for Detecting XSS Attacks on IoT over 5G NetworksMahmoud AlJamal0Rabee Alquran1Ayoub Alsarhan2Mohammad Aljaidi3Mohammad Alhmmad4Wafa’ Q. Al-Jamal5Nasser Albalawi6Department of Cybersecurity, Science and Information Technology, Irbid National University, Irbid 21110, JordanDepartment of Information Technology, Faculty of Prince Al-Hussien bin Abdullah, Hashemite University, Zarqa 13133, JordanDepartment of Information Technology, Faculty of Prince Al-Hussien bin Abdullah, Hashemite University, Zarqa 13133, JordanDepartment of Computer Science, Faculty of Information Technology, Zarqa University, Zarqa 13110, JordanDepartment of Information Technology, Faculty of Prince Al-Hussien bin Abdullah, Hashemite University, Zarqa 13133, JordanFaculty of Science and Technology (FST), Universiti Sains Islam Malaysia (USIM), Nilai 71800, MalaysiaDepartment of Computer Science, Faculty of Computing and Information Technology, Northern Border University, Rafha 91911, Saudi ArabiaAs 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.https://www.mdpi.com/1999-5903/16/12/482Internet of Things (IoT)5G networksXSS attacksmachine learning (ML)cybersecurity |
| spellingShingle | Mahmoud AlJamal Rabee Alquran Ayoub Alsarhan Mohammad Aljaidi Mohammad Alhmmad Wafa’ Q. Al-Jamal Nasser Albalawi A Robust Machine Learning Model for Detecting XSS Attacks on IoT over 5G Networks Future Internet Internet of Things (IoT) 5G networks XSS attacks machine learning (ML) cybersecurity |
| title | A Robust Machine Learning Model for Detecting XSS Attacks on IoT over 5G Networks |
| title_full | A Robust Machine Learning Model for Detecting XSS Attacks on IoT over 5G Networks |
| title_fullStr | A Robust Machine Learning Model for Detecting XSS Attacks on IoT over 5G Networks |
| title_full_unstemmed | A Robust Machine Learning Model for Detecting XSS Attacks on IoT over 5G Networks |
| title_short | A Robust Machine Learning Model for Detecting XSS Attacks on IoT over 5G Networks |
| title_sort | robust machine learning model for detecting xss attacks on iot over 5g networks |
| topic | Internet of Things (IoT) 5G networks XSS attacks machine learning (ML) cybersecurity |
| url | https://www.mdpi.com/1999-5903/16/12/482 |
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