# A Robust Machine Learning Model for Detecting XSS Attacks on IoT over 5G Networks
## 2 Related Work  
The proliferation of 5G networks and the increasing use of IoT technologies have completely changed the way we connect with and manage our environment. Cross-Site Scripting (XSS) attacks, in particular, pose a serious threat to the security and privacy of IoT devices. Nevertheless, this enhanced connectedness has also brought forth new security vulnerabilities. In order to solve these issues, this section provides a review of the current literature on the use of deep learning and machine learning approaches to mitigate XSS attacks on IoT systems, emphasizing solutions designed for the unique constraints and requirements of 5G IoT networksIn their approach, [PERSON] et al. [24] emphasizes the importance of intrusion detection in IoT systems, especially in smart city settings. They present an effective technique that combines semi-supervised learning and a dynamic linear graph neural network (DLGNN). The effectiveness of this method is examined on six distinct datasets, including NF-ToN-IoT-V2. The model's impressive detection accuracy of 95.70% for XSS attacks indicates that it has the potential to improve IoT security.  
[PERSON] et al. [25] focus on detecting injection attacks in IoT systems by introducing an intrusion detection approach. The study explored two feature selection methods--constant removal and recursive feature elimination--using machine learning classifiers like SVM, Random Forest, and Decision Tree. When tested on the AWID dataset, the decision tree classifier showed impressive performance, accurately detecting injection attacks with a 99% success rate using only eight selected features. This study emphasizes the importance of effective feature selection in improving intrusion detection for IoT systems, particularly in protecting smart cities from security threats.  
[PERSON] et al. [26] make a significant contribution to IoT system security by presenting a deep autoencoder dense neural network algorithm specifically designed to detect intrusions or attacks in 5G networks. Their experiments, conducted using the AWID-CLS-R dataset, revealed a remarkable detection accuracy of 99.87% for injection attacks. By leveraging the neural network's ability to learn complex patterns, this approach successfully differentiates between normal network traffic and malicious activity, underscoring its value in enhancing network security.  
The growing number of cyberattacks targeting IoT networks and devices is addressed by [PERSON] et al. [27]. The relevance of prediction time in anomaly-based network intrusion detection systems (NIDS) is highlighted, along with the function that machine learning (ML) plays in NIDS as a preventive measure. Their work presents a model that selects 13 features using the Feature Importance model, which yields a 98% detection accuracy for XSS attacks. This demonstrates how effectively their method minimizes the computing strain while detecting risks.  
[PERSON] et al. [28] conducted research using the NF-ToN-IoT-V2 dataset to develop a machine learning-based model for detecting cyber-attacks. Their model, applied across multiple datasets, demonstrated a detection rate of up to 96.83% for identifying XSS injection attacks, further supporting the effectiveness of machine learning in securing IoT systems.  
The assessment of related work highlights the critical role of machine learning and deep learning approaches in securing 5G IoT networks from Cross-Site Scripting (XSS) threats. The reviewed studies underscore the dangers linked to the growing adoption of IoT technologies and the way in which 5G networks are redefining management and interactions. Moreover, these studies underscore the importance of feature selection in enhancing intrusion detection capabilities. The results demonstrate the effectiveness of AI techniques in supporting network security and protecting IoT devices, particularly in smart cities, where dynamic and evolving security risks are prevalent.