## 8 Conclusions  
This study utilized the CSE-CIC-IDS2018 dataset, comprising 80 features and over 16 million records, to evaluate Intrusion Detection Systems in computer networks, emphasizing its relevance to computer science. This research involved data cleaning and three feature selection approaches. In the initial stage, by utilizing only five features, a remarkable accuracy of 98% in identifying safe traffic and computer attacks was achieved; this demonstrates the effectiveness of feature selection and its impact on accurate intrusion detection. Additionally, this study successfully built a model to detect intrusion in 1 s using only nine features, showcasing efficient performance in time.  
Furthermore, in subsequent stages, a model with approximately 99% accuracy was constructed within a reasonable time frame by selecting ten features and employing different feature selection techniques; this highlights the importance of accuracy and time considerations in Intrusion Detection Systems.  
Future research should continue refining feature selection methods and exploring data cleaning. Various techniques can be employed to optimize system performance and determine which features influence accurate intrusion detection most. These techniques aim to enhance the overall effectiveness of Intrusion Detection Systems by optimizing system performance and focusing on identifying key features that contribute significantly to accurate detection. One valuable suggestion arising from this study is to consider the priorities of network experts or managers. Depending on their network requirements, they can prioritize either time or accuracy. If time is crucial, they can adopt approaches that prioritize faster detection of attacks. Conversely, investing more time in the detection process can yield higher accuracy if accuracy is paramount. This recommendation gives network experts an essential tip for making informed decisions based on their network's needs.  
Regarding computer networks, especially data centers, using intrusion systems is essential for reducing the damage caused by cyberattacks. One of the most important aspects of preventing intrusions on servers and networked devices is the timely and accurate identification of such intrusions. Intrusion Detection Systems become much moreefficient when the number of features required for detection is reduced. This decrease prevents potential slowdowns in intrusion detection operations by reducing excessive memory and CPU consumption and increasing detection speed. For this reason, it is critical to detect assaults on computer networks as soon as possible to prevent serious harm. Furthermore, reaching higher threat detection accuracy gives network specialists more confidence and presents chances for cost savings by switching resources from human involvement to Intrusion Detection Systems' strong capabilities.  
Acquiring, organizing, simulating, analyzing, and drawing conclusions from data; formulating problems and providing feedback on outcomes: H.N.M. and M.A.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The dataset can be accessed through the [[https://www.kaggle.com/datasets/dhoogla/csecicids2018](https://www.kaggle.com/datasets/dhoogla/csecicids2018)]([https://www.kaggle.com/datasets/dhoogla/csecicids2018](https://www.kaggle.com/datasets/dhoogla/csecicids2018)) provided in the article.  
The authors declare no conflict of interest.