## 1 Introduction  
Large-scale local area networks use anomaly detection as a primary technique for solving security issues. However, it can be challenging to extract accurate traffic data for anomaly identification [1]. Intrusion detection in computer networks is crucial as it affects communication and security; yet, finding network intrusions remains a challenge [1]. Network intrusion detection is complicated due to the need for significant data for training advanced machine learning models [1]. The emergence of new threats that older systems cannot detect presents significant difficulties in network intrusion detection [2]. Therefore, this paper aimed to compare multiple techniques to develop an effective network Intrusion Detection System. The increasing complexity and volume of real-time traffic, driven by the rapid development and adoption of technologies such as 5G, IoT, and Cloud Computing, have led to more sophisticated and varied cyberattacks, posing significant challenges to cyberspace security [3]. Network Intrusion Detection Systems (NIDSs), which serve as the firewall's backup line of defense, function to develop strategies, provide real-time monitoring, swiftly identify malicious network attacks, and implement dynamic security measures [3]. Cybersecurity research has become essential given how much networks are used in the modern world [4]. Current Intrusion Detection Systems (IDSs) have failed to identify new attacks, improve detection accuracy, and lower false alarm rates despite years of research. To solve these issues, many scholars have concentrated on developing IDSs with machine learning methods [4]. Machine learning offers accurate and automatic identification of differences between normal and abnormal data, including recognizing unidentified attacks, while providing excellent generalizability [4]. Feature Selection (FS) techniques play a crucial role in data preprocessing by enabling effective data reduction and supporting the identification of precise data models [5]. Although extensive searching for the ideal feature subset is often impractical, the literature offers numerous search algorithms for overcoming this difficulty [5]. Four widely used techniques employed in feature selection for effective network intrusion detection are the filter, wrapper, embedding, and hybrid methods [5]. The dataset KDD CUP'99 challenge results demonstrated the superiority of Long Short-Term Memory (LSTM) classifiers over other static classifiers, underscoring the significance of LSTM networks for intrusion detection [6]. LSTM networks can learn from past data and establish connections between connection records, making them compelling in intrusion detection [6]. Hyperparameters govern the learning process and model quality in almost all machine learning algorithms. Adjusting these hyperparameters can lead to the generation of infinite models with varying performance levels and learning rates, depending on the specific techniques used for machine learning [7]. Exponential data growth in recent times has posed significant challenges in the classification process [8]. Feature selection solves this problem, improving data classification accuracy while reducing data complexity [8] because many time-consuming features play a role in the detection process. In order to increase the efficacy of Network Intrusion Detection Systems (NIDSs), feature selection is essential [9]. The feature selection approach influences the time needed to observe traffic behavior and the degree of accuracy improvement. The four approaches to feature selection include wrapper, hybrid techniques, filtering, and embedding [9]. This paper's primary contribution is its attempt to pinpoint a subset of features that are most effective at distinguishing between various attacks and regular network traffic while maintaining the fewest overall features possible.