## 1 Introduction  
The exponential growth of digital communication networks has significantly transformed the way information flows within interconnected systems. These networks, ranging from personal devices to enterprise infrastructures, manage vast volumes of data in real time, playing a crucial role in facilitating daily operations across various sectors [1, 2]. Cloud platforms, corporate networks, and other digital frameworks together constitute the core of this interconnected digital environment [3]. However, as the complexity and scale of these systems increase, their susceptibility to various cyber threats also rises. Modern cyberattacks, characterized by their sophistication and persistence, pose significant challenges to network security [4]. Traditional security measures are increasingly strained by threats like Advanced Persistent Threats (APTs), zero-day exploits, and constantly evolving attack strategies. For example, APTs are characterized by prolonged, targeted intrusion efforts that are challenging to detect due to their subtle and covert tactics. Similarly, zero-day vulnerabilities exploit undiscovered software flaws, enabling attackers to evade established security measures [5; 6]. Additionally, dynamic attack strategies, in which attackers adjust their methods during an operation, make detection and mitigation more challenging [7]. Compounding these issues is the high-dimensional nature of traffic data in modern networks, which further complicates the process of identifying threats. Every data packet can contain numerous attributes, such as source and destination IP addresses, along with behavioral indicators, resulting in an overwhelming volume of information that conventional approaches find difficult to handle [8]. Although real-time anomaly detection is essential, traditional IDS often face challenges due to their high computational demands and constraints, limiting their overall effectiveness. The increasing complexity of network traffic necessitates the creation of scalable and efficient IDS capable of real-time operation, adapting to emerging attack patterns, and managing the growing volume and diversity of data. Today, cybersecurity threats are not only varied but also more focused and destructive [9; 10]. For instance, Distributed Denial of Service (DDoS) attacks flood systems with fraudulent traffic, making them unreachable for authorized users. Brute force attacks methodically target authentication systems, and malware infections infiltrate networks to steal, damage, or erase data [11; 12]. To highlight the scope of these threats, this research uses the CSE-CIC-IDS2018 dataset [13], a detailed collection containing more than 16 million examples of both benign and malicious network traffic. This dataset contains a range of attack families, including Botnet, Web Attacks, Infiltration, and DDoS, offering an accurate portrayal of the challenges encountered by contemporary networks. This diversity within the dataset highlights the need for resilient and flexible detection systems capable of effectively identifying both known and emerging threats. IDS act as the primary defense against such threats by constantly analyzing network traffic to detect any abnormal or suspicious behavior [14], as illustrated in Figure 1. However, traditional IDS methods are quickly losing their effectiveness. Signature-based systems depend on predefined attack patterns, which limits their ability to detect new or evolving threats. Although rule-based approaches offer flexibility, they typically necessitate significant manual configuration and face challenges in scaling to meet the increasing complexity of network environments. Both approaches struggle with high-dimensional traffic data, resulting in processing challenges and an increased likelihood of false positives or negatives. To address these limitations, IDS based on machine learning have surfaced as a viable alternative.