# Enhancing IoT Network Security Using Feature Selection for Intrusion Detection Systems
## 5 Discussion  
Intrusion detection is a vital process in IoT security as it is employed to effectively recognize and mitigate possible threats and malicious attacks. Notably, the absence of overt predictor variables until an attack occurs underscores the need for early detection and preventive strategies [23]. Interventions have proved to be significantly more effective when threats and attacks are identified at nascent stages [12].  
In many practical scenarios, it might be challenging or cost-prohibitive to analyze a comprehensive set of features. Thus, building predictive models using reduced data offers a promising approach for the early screening and detection of abnormal activities. By identifying those areas most vulnerable to attack through simple indicators and by  
Figure 6: Correlation coefficient heatmap indicating the correlations between attack type and the identified important features.  
recommending further precise monitoring, this approach can help reduce unnecessary cybersecurity solutions and save costs.  
Our aim is to study feature selection in IoT network traffic data with the aim of developing a predictive model that could contribute to the early warning and early detection of attacks. Our results have a double application. First, we focused on the key features in IoT network traffic data that can help in detecting malicious attacks. The properties of malicious attacks recognized by the feature selection algorithm can assist IT specialists to better defend these networks. Innovation in machine learning does not always mean using the most sophisticated methods or complicated feature engineering [38]. Sometimes, simplifying the development of predictive models shows an important form of innovation for improving their popularity and usability. From the comparative analysis, deploying various feature selection methods on an average-power device is computationally inexpensive and fast. It also has the advantage of decreasing the complexity of the model and boosting its practicality. The data from these feature selection algorithms indicated optimal predictive performance upon the inclusion of 16 predictor variables.  
Second, feature selection was used to develop an effective machine and deep learning classifier-based IDS. Herein, we present an MLP algorithm built with the help of feature selection methods. This approach offers higher predictive accuracy over a model with a full set of features, a finding in alignment with contemporary studies on feature selection with ML [38]. Our approach achieved remarkable performance with respect to detection and false alarm rates. The results showed that various feature selection methods are capable of identifying relevant features in network traffic data and training a powerful ML-based IDS. Furthermore, the broad feature selection process and rigorous validation reaffirm the model's robustness and reliability.  
In this study, we identified significant correlations between various network traffic features and specific types of attacks through the application of feature selection algorithms and correlation analysis. These correlations reveal how certain traffic patterns and behaviors are closely linked to distinct cyberattack types, providing insights that enhance the performance of intrusion detection systems (IDSs), particularly in resource-constrained IoT environments. The following subsections delve into each identified correlation, discussing its implications for IoT network security, along with supporting evidence from the literature, and reviewing the potential for improving detection accuracy and robustness against evolving threats.