CORTEXA
← Browse
crossrefFuture Internet2024-12-23Cited by 19

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, Nasser Albalawi

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.

View free PDFSource page

Related papers

crossrefFuture Internet2026-01-07Cited by 7

A Multiclass Machine Learning Framework for Detecting Routing Attacks in RPL-Based IoT Networks Using a Novel Simulation-Driven Dataset

Niharika Panda, Supriya Muthuraman

The use of resource-constrained Low-Power and Lossy Networks (LLNs), where the IPv6 Routing Protocol for LLNs (RPL) is the de facto routing standard, has increased due to the Internet of Things’ (IoT) explosive growth. Because of the dynamic nature of IoT deployments and the lack…

View free PDFSource page
crossrefFuture Internet2026-02-19

A Systematic Review of Machine-Learning-Based Detection of DDoS Attacks in Software-Defined Networks

Surendren Ganeshan, R Kanesaraj Ramasamy

Software-Defined Networking (SDN) has emerged as a fundamental architecture for future Internet systems by enabling centralized control, programmability, and fine-grained traffic management. However, the logical centralization of the SDN control plane also introduces critical vul…

View free PDFSource page
crossrefFuture Internet2026-04-27Cited by 1

Enhancing Network Intrusion Detection with Quantum Machine Learning: A Comprehensive Survey of Methods, Metrics, and Applications

Antanios Kaissar, Ali Bou Nassif, Ahmed Bouridane

Quantum computing introduces new computational capabilities that can support advanced cybersecurity solutions when combined with machine learning. In recent years, quantum machine learning (QML) has emerged as a promising approach for enhancing network intrusion detection systems…

View free PDFSource page
crossrefFuture Internet2026-02-21Cited by 4

Machine Learning-Driven Intrusion Detection for Securing IoT-Based Wireless Sensor Networks

Yirga Yayeh Munaye, Abebaw Demelash Gebeyehu, Li-Chia Tai, Zemenu Alem Abebe, Aeneas Bekele Workneh, Robel Berie Tarekegn, et al.

Wireless sensor networks (WSNs) have become a critical component of modern Internet of Things (IoT) infrastructures; however, their constrained resources and distributed deployment expose them to various cyber threats. In this work, we present a machine learning-driven intrusion…

View free PDFSource page
crossrefFuture Internet2026-04-28

Explaining Seasonal 5G Path Loss in a Vineyard: From Empirical Models to Interpretable Machine Learning

Daniel Schneider, Ali Imran Jehangiri, Daniel Müller, Hannes Frey, Maria Anna Wimmer

Radio network planning is critical for 5G deployments, particularly for temporary installations in rural areas where terrain and vegetation significantly impact signal propagation. While empirical path loss (PL) models characterize propagation environments through scenario-specif…

View free PDFSource page
crossrefFuture Internet2025-07-18Cited by 14

Enhancing Security in 5G and Future 6G Networks: Machine Learning Approaches for Adaptive Intrusion Detection and Prevention

Konstantinos Kalodanis, Charalampos Papapavlou, Georgios Feretzakis

The evolution from 4G to 5G—and eventually to the forthcoming 6G networks—has revolutionized wireless communications by enabling high-speed, low-latency services that support a wide range of applications, including the Internet of Things (IoT), smart cities, and critical infrastr…

View free PDFSource page