## 1 Introduction  
The Internet has become an essential component of everyday life, facilitating diverse electronic services including online education, business activities, shopping, job searches, and banking. This increasing reliance on the Internet has also heightened the cyber threat landscape, leading to a rapid evolution in security breaches and making information systems and computer networks vulnerable to sophisticated attacks [1]. Recently, there has been a significant increase in data breaches [2] and privacy violations [3, 4], prompting the increased scrutiny of cybersecurity measures. Despite the deployment of security measures like user authentication mechanisms, encryption techniques, firewalls, and antivirus programs, these tools often fall short in defending against all types of cyber threats [5]. Intelligent security systems, like intrusion detection systems (IDSs), have become essential to mitigate these risks. IDSs are a solution, either hardware or software, intended to secure network and information systems from a variety of threats. They operate by monitoring network traffic, analyzing user and system activities, and detecting abnormal patterns or common attack vectors. By identifying and responding to potential malicious actions, IDSs are crucial for maintaining the security of networks and ensuring the confidentiality, integrity, and availability of information systems. Their capability to detect and prevent threats is essential for upholding security policies and mitigating risks to computer systems [6, 7, 8]. In general, IDSs are classified into two primary categories: host intrusion detection systems (HIDSs) and network intrusion detection systems (NIDSs) [9]. HIDSs are installed on individual hosts to track all activities occurring on the host itself. Conversely, NIDSs are implemented to protect all network-connected devices from potential security breaches. IDSs can also be categorized according to their detection methodologies: rule-based or signature-based IDSs and anomaly-based IDSs [10]. Rule-based IDSs detect familiar attack patterns by comparing them against a database of stored signatures [11], but they struggle with zero-day or novel attacks when their signature databases are not updated. In contrast, anomaly-based IDSs analyze network activities to identify patterns of normal and abnormal behavior, enabling them to effectively detect unknown types of attacks [12; 13]. The current intrusion detection datasets still suffer from the curse of dimensionality, which arises when handling high-dimensional data and significantly affects IDS performance. Conventional feature extraction methods, including linear discriminant analysis and principal component analysis, are commonly used to address high-dimensional data in IDS contexts, but they often fall short. Deep learning (DL) is a groundbreaking subset of machine learning that has achieved significant success in automating feature extraction from data [14]. DL employs multilayer neural networks that learn data representations through iterative mathematical operations. It excels in processing large-scale data efficiently, demonstrating successes across diverse fields like medical imaging [15], natural language processing [16], sentiment analysis [17], healthcare [18], and image recognition [19]. Convolutional Neural Networks (CNNs) play a vital role in DL, particularly in performing feature extraction [20]. Although effective, CNNs face challenges such as vanishing and exploding gradients, which are effectively addressed by architectures like the Residual Network (ResNet) [21]. ResNet can extract deep features while mitigating gradient-related problems, though its complex structure with multiple layers and parameters presents implementation challenges. Furthermore, relying on a single algorithm in IDS frameworks may compromise efficiency and hinder the identification of complex, multiclass attacks.