## 1 Introduction  
The Internet of Things (IoT) constitutes a network that facilitates the connection of physical objects with computing capabilities to the Internet [1]. As illustrated in Figure 1, the IoT finds versatile applications across diverse industries such as smart homes and cities, energy and productivity, transportation, production and logistics, livestock and animals, agriculture, and the insurance sector [2]. While the IoT offers enhanced efficiency through intelligent and remote management, the absence of adequate security measures leads to increased security risks, such as cyberattacks. Consequently, devices connected to IoT networks become vulnerable to such attacks [3]. Security within IoT networks and strategies to mitigate the risks of cyberattacks represent prominent topics in cybersecurity. Specific IoT applications, particularly those within the industrial IoT (IIoT), involve mission-critical operations that necessitate elevated security measures. Securing IoT and IIoT networks presents unique challenges due to their reliance on lightweight communication protocols and constrained computational resources. Traditional security mechanisms like firewalls and encryption, while essential for data confidentiality and integrity, often demand significant processing power and storage, making them impractical for these environments. Moreover, attacks such as DDoS or protocol-level exploits can bypass encrypted channels, exposing vulnerabilities that encryption alone cannot address. To overcome these limitations, a complementary approach is critical. The intrusion detection system (IDS) augments encryption by monitoring traffic patterns to detect and mitigate threats that evade conventional safeguards. This dual-layered strategy ensures robust security without overwhelming limited resources, addressing the inherent challenges of IoT/IIoT ecosystems while enhancing resilience against evolving cyber threats [4; 5]. As shown in Figure 2, intrusion detection systems monitor system or network events to help to identify malicious activities [6]. Assessing intrusion detection techniques is essential, with datasets being crucial for measuring the accuracy and effectiveness of security methods. However, the limited availability of real-world datasets for evaluating intrusion detection approaches in IoT and IIoT networks poses a considerable challenge. Figure 1: Application of the Internet of Things in different industries [2]. Figure 2: Application of an IDS in Internet of Things networks to mitigate attacks [6]. The growing prevalence of threats and risks in IoT and IIoT networks highlights the need for effective detection techniques and traffic attack prevention mechanisms in these environments. Machine learning is one such approach, providing a powerful and efficient alternative to traditional security methods that depend on manual oversight and coding. However, selecting an appropriate dataset is crucial for effective training. Therefore, using datasets that accurately represent real network events is essential for intrusion detection in IoT and IIoT networks. The rapid expansion of IoT/IIoT networks necessitates robust IDSs capable of adapting to sophisticated cyberattacks. To address the scarcity of representative datasets for IoT/IIoT security research, we leverage the DNN-EdgelIoT dataset [7], a comprehensive benchmark specifically designed for machine-learning-based IDSs. Introduced by [PERSON] et al. (2022) [7], this dataset captures diverse attack scenarios (e.g., DDoS, SQL injection, malware) and normal traffic from a realistic IoT/IIoT testbed, spanning 2.2 million connection samples. Unlike conventional datasets, DNN-EdgelIoT incorporates protocol-specific features (e.g., MQTT, HTTP) and system-level metrics (e.g., CPU usage), making it uniquely suited for evaluating spatial and temporal attack patterns in resource-constrained environments.