# Enhancing IoT Network Security Using Feature Selection for Intrusion Detection Systems
## 6 Conclusions  
In this paper, we present a comprehensive approach to enhancing the performance of anomaly-based IDSs in IoT environments via a feature selection process that selects valid features and rejects invalid ones. This paper also introduces the RT-IoT2022 dataset, which is specifically designed for intrusion detection in a real-time IoT environment to fill the research gap noted in previous studies. The proposed approach is validated on this dataset, demonstrating the need to use more recent and specific datasets to investigate the security challenges associated with real-time IoT systems.  
Whereas prior research has mostly focused on a single feature that is identified using a single-feature selection method, this paper combines the gain ratio, information gain, CFS, and symmetrical uncertainty to comprehensively select multiple features. The use of feature selection makes the dataset suitable for resource-constrained IoT devices by streamlining the dataset without compromising performance.  
This paper adds a layer of granularity and depth that has not previously been discussed. For example, the specific correlation between the key features, such as 'fwd_init_window_size', and various types of attacks has been analyzed. This correlation is a major requirement that has not been significantly addressed in previous research.  
In conclusion, this study offers an efficient, highly accurate, and scalable IDS framework that contributes to the growing body of research in the domain of IoT network security. In the future, the methodology should be tested using other large IoT datasets from multiple sources, such as IoT-23 and UNSW-NB15, to capture latent features related to the field. In addition, the application of selected features to classify incoming network traffic in the IoT environment as either normal or abnormal should be explored. Both machine learning classifiers and deep learning models can be implemented for the task of classification. Various models, such as artificial neural networks, deep neural networks, and TabNet, can be leveraged to further improve the accuracy and operational efficiency of IDSs in real-time IoT systems. This approach may enhance the detection and classification of IoT attacks, thereby improving the security of IoT systems. The proposed approach can be deployed in real-time IoT systems by leveraging the reduced feature set for the efficient processing of resource-constrained devices. It integrates lightweight machine learning models and streaming analytics to classify traffic dynamically, enabling the immediate detection and mitigation of IoT-related cyberattacks.  
Conceptualization, M.A. and F.A.; methodology, M.A. and F.A.; software, M.A. and F.A.; validation, M.A. and F.A.; formal analysis, M.A. and F.A.; investigation, M.A. and F.A.; resources, M.A. and F.A.; data curation, M.A. and F.A.; writing--original draft preparation, M.A. and F.A.; writing--review and editing, M.A. and F.A.; supervision, M.A. and F.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.