## 1 Introduction  
_Research Background_  
As the internet has expanded rapidly, cyberattacks on computer systems and networks have increased in frequency. To address these risks, network systems employ intrusion detection systems (IDSs) to detect and stop malicious activity. In our digital age, where dependence on technology is essential, creating secure and reliable programs, frameworks, and networks is one of the primary challenges [1]. An important deterrent to network intrusion is the ability to detect faults in a system. Intrusion detection is defined as the process of identifying and responding to malicious activity targeting computing and network resources. The rapid increase in network traffic and resulting security threats make it more difficult for IDSs to accurately detect hostile attacks. The idea is to provide up-to-date information on machine learning and deep learning-based intrusion detection systems to create a baseline for further researchers exploring this important area [2]. Studies have shown that lightweight machine learning models, such as decision trees and support vector machines, can be employed for intrusion detection. These models are computationallyefficient while maintaining acceptable detection accuracy, and they can adjust to shifting patterns by learning from past data [3]. Machine learning-based intrusion detection systems provide a learning-based method to identify attack classes by using learned normal and attack behavior. Machine learning-based intrusion detection systems use supervised learning techniques to generate a wide representation of known threats. The maintenance of the signature database is another requirement for these types of intrusion detection systems, and it increases user load [4]. Recently, there has been an outstanding advancement in intrusion detection techniques, achieved to a great extent by the remarkable capabilities of deep learning models. These deep learning models have introduced another era of accuracy and detection within various systems. Deep learning models, such as long-short-term memory (LSTM), recurrent neural networks (RNNs), convolutional neural networks (CNNs), and deep neural networks (DNNs), have demonstrated remarkable improvements in their learning behaviors and detection rates [5]. This has led to a shift in cybersecurity, with deep learning-based intrusion detection systems exhibiting high performance and resilience against emerging threats. Recent advancements in hardware architectures have facilitated the integration and deployment of sophisticated models that can ensure robust and reliable protection against cyber threats. Additionally, by utilizing deep learning models, the system can effectively detect anomalies within datasets, enabling the accurate and precise identification of security threats. The motivation for employing lightweight machine learning models alongside deep learning models stems from the need to balance detection performance with resource efficiency. Models such as k-nearest neighbor (KNN), support vector machines (SVMs), multilayer perceptron (MLP), logistic regression (LR), and random forest (RFs) are chosen for their ability to deliver high accuracy while minimizing computational costs, making them suitable for real-time intrusion detection applications. Furthermore, deep learning and machine learning approaches have been increasingly applied to a range of Internet of Things (IoT) security solutions. Combining big data analytics with machine learning/deep learning-based methods has yielded significant outcomes in cybersecurity. Deep learning-based algorithms, in particular, have gained considerable attention due to their superior pattern-extraction capabilities, outperforming machine learning in various studies [6; 7]. This paper provides a comparative analysis of deep learning and lightweight machine learning models used for intrusion detection systems.