## 1 Introduction  
Intrusion detection systems (IDSs) represent proactive defence mechanisms that leverage advanced data analytics to identify malicious activity, suspicious behaviour, abnormal patterns, policy violations, and security breaches within a network domain or a host system. The escalating adoption of diverse network devices has increased systems' vulnerability, making them more susceptible to exploitation by hackers to compromise the CIA triad [1] in terms of targeting the Confidentiality, Integrity, and Availability of the systems. The two main categories of intrusion detection in an IDS are: (1) anomaly detection, which involves detecting deviations from normal behaviour; and (2) misuse detection, which involves detecting known malicious patterns.  
The IDS, however, suffers from a number of deficiencies, including:  
* Low accuracy, high false positive rate, or high amount of model's overfitting [2; 3; 4; 5].
* Inefficiency in dealing with big data, which is common in network attacks [2; 6; 7].
* Keeping a stable detection rate when different volumes of traffic and types of intrusion are encountered [6].
* Incapability to detect unknown zero-day attacks [6].  
In this work, we focus on enhancing the performance of the IDS by reducing overfitting and increasing the accuracy of the classification models using hybrid machine learning-based approaches. To assess the efficacy of our detection mechanism, we conducted a controlled experiment on a real-world network traffic dataset. Our main research question is whether hybrid machine learning-based approaches can enhance intrusion detection accuracy by reducing network traffic features and the overfitting of the model.  
Some of the major benchmarking IDS datasets that are often utilized in research are KDD'99 [8], NSL-KDD [9] (an improved version of the KDD99), CIC-IDS2017 [10], and CSE-CIC-IDS2018 [11]. The KDD99 dataset has four types of intrusion categories: DOS (Denial of Service attack), R2L (Remote to Local attack), U2R (User to Root attack), and Probe. The CSE-CIC-IDS (The Communications Security Establishment (CSE) and the Canadian Institute for Cybersecurity (CIC)) 2018 dataset [11] includes seven types of attacks, including Brute-force, Botnet, DoS, DDoS, Heartbleed, Web attacks, and infiltration of the network from inside. The sub-attack types for KDD99 and CSE-CIC-IDS2018 are presented in Tables 1 and 2.  
_Contributions:_ We perform three phases of data preprocessing, feature selection and reduction, and attack detection to find the best combination of machine learning approaches. We select the CSE-CIC-IDS2018 dataset [11] for our experiment since it has more recent attack patterns. All implementation and experiments are performed using R studio. We implement various feature reduction approaches, including Boruta, Random Forest, LASSO, and deep learning-based methods, and executed them on the CSE-CIC-IDS2018 dataset. We investigate the issue of model overfitting by utilizing different single models and ensemble classifiers such as LASSO, XGBoost, and dropout mechanisms in deep neural networks to combat overfitting. We demonstrate the effectiveness of Decision Tree classifier with the least amount of overfitting on the reduced sets constructed from autoencoders.  
_Outline:_ The rest of this paper is organized as follows. Section 2 reviews the related work. Section 3 describes materials and methods for conducting this research. Section 4 provides the results of our experiments. Section 5 discusses our findings. Finally, Section 6 draws conclusions and future works.