## 2 State of the Art  
Developing intrusion detection systems is a critical step toward securing IoT devices. To create an effective research environment for this purpose, a labeled dataset plays an indispensable role. The utilization of real-world datasets that accurately reflect IoT applications is crucial in evaluating the competency and efficiency of security methods. However, the unavailability of such datasets poses a significant obstacle to evaluating intrusion detection systems for IoT applications. The lack of labeled datasets restricts researchers from empirically validating and evaluating intrusion detection methods tailored to IoT applications, thereby hindering the development of more sophisticated methods. In essence, access to labeled datasets is essential for researchers to evaluate the effectiveness of intrusion detection methods for IoT devices and create robust security solutions [9]. [PERSON] and [PERSON] [10] used data mining and machine learning (ML) approaches for intrusion detection systems (IDSs) in their cyber security study. Due to privacy concerns, they determined that the absence of labeled datasets is a key barrier in building anomaly-based intrusion detection methods, as most large corporations do not share their IoT datasets with the academic community [11]. Popular datasets, such as KDDCUP99 and NSL-KDD [12], UNSW-NB15 [13], and ISCX [14], were developed to fill this gap; however, they lack IoT-specific components, such as sensor readings and IoT network traffic. Although several research studies [15; 16] utilized these datasets to assess intrusion detection methods, these datasets do not represent the pure attributes because none of them includes any lot system in their functional prototypes. IoT data for categorizing IoT devices based on network metrics were provided by [PERSON] et al. [15]. The authors created a testbed for smart homes to collect IoT traffic and utilized flow-based traits to identify each IoT device, concluding that each item exhibits recognizable patterns in its traffic flows, such as activity phases and volume changes. As a result of their creation with the goal of device categorization, these datasets do not, however, contain any attack patterns. To address the difficulties raised earlier, numerous researchers, most notably [PERSON] et al. [17] and [PERSON] et al. [16], have presented IoT network-based IoT datasets that incorporate attack patterns. They created an IoT-based dataset aimed at identifying DoS assaults in an IoT network, in which they gathered several types of normal and DoS attack traffic, including the TCP SYN attack and Ping of Death. To collect data, they mimicked a smart home setting. [PERSON] et al. [18] have presented the Aegean WiFi Intrusion Dataset (AWID), which was collected from a Small Office/Home Office (SOHO) environment utilizing 802.11 wireless connections. This dataset encompasses various devices, including desktop computers, laptops, smartphones, tablets, and smart TVs. Apparently, this collection only contains MAC layer frame traces without a proper IoT device sensor data infrastructure. On the other hand, [PERSON] et al. [17] developed a BoT-IoT dataset that includes both normal and attack traffic. The dataset contains approximately 72 million records of network activity from a modeled IoT ecosystem, and a mini version with roughly 3.6 million records is available for review. [PERSON] et al. [19] created a network-driven dataset of IIoT systems for cybersecurity assessment. To replicate real-life industrial applications, the dataset was produced by simulating genuine industrial systems, which include several IIoT sensors and actuators, a human-machine interface (HMI), a logger, and an alerting device. The dataset was subjected to different popular machine learning methods. For binary classification, the RF model had the maximum accuracy of 99.99%, while the NB model had the lowest accuracy of 97.48%. The testbed included backdoor, command injection, denial-of-service, and reconnaissance threats.