## 1 Introduction  
Despite the significant benefits that IoT and Industrial IoT (IIoT) networks bring to citizens, society, and industry, the fact that these networks incorporate a wide range of different communication technologies (e.g., WLANs, Bluetooth, and Zigbee) and types of nodes/devices (e.g., temperature/humidity sensors), which are vulnerable to various types of security threats, raises many security and privacy challenges in IoT/IIoT-based systems. For instance, attackers may compromise IoT/IIoT networks in order to manipulate sensing data (e.g., by injecting fake data) and cause malfunction to the IoT/IIoT-based systems that rely on the compromised IoT/IIoT networks. It is worthwhile to mention that IoT/IIoT networks can become an attractive target of attackers with a wide spectrum of motivations ranging from criminal intents aimed at financial gain to industrial espionage and cyber-sabotage. Therefore, security solutions protecting IoT/IIoT networks from attackers are critical for the acceptance and wide adoption of such networks in the coming next years. Nevertheless, the high resource requirements of complex and heavyweight conventional security mechanisms cannot be afforded by (i) the resource-constrained IoT/IIoT nodes (e.g., sensors) with limited processing power, storage capacity, and battery life; and/or (ii) the constrained environment in which the nodes are deployed and interconnected using lightweight communication protocols. Consequently, there is an urgent need for novel security mechanisms, such as accurate and efficient anomaly-based intrusion detection systems(AIDSs) tailored to the resource-constrained characteristics of IoT/IIoT networks, to be developed in order to address the pressing security challenges of IoT/IIoT networks with reasonable cost, in terms of processing and energy, before IoT/IIoT networks gain the trust of all involved stakeholders and reach their full potential in the market [1, 2, 3]. However, there is a lack of up-to-date, representative and well-structured IoT/IIoT-specific datasets that are publicly available to the research community and constitute benchmark datasets for training and evaluating machine learning (ML) models used in AIDSs for IoT/IIoT networks [4, 5]. This lack of benchmark IoT/IIoT datasets constitutes a significant research gap that should be addressed in order to develop more accurate and efficient IoT/IIoT-specific AIDS whose effectiveness is evaluated based on their performance to detect IoT/IIoT attacks which is a process reliant on comprehensive IoT/IIoT-specific datasets. In fact, although several datasets, such as KDDCUP99 [6], NSL-KDD [7], UNSW-NB15 [8], and CICD2017 [9] have been created over the past two decades for evaluation purposes of network-based intrusion detection systems (IDSs), they do not include any specific characteristics of IoT/IIoT networks as these datasets do not contain sensors' reading data or IoT/IIoT network traffic [4, 5]. To respond to this major issue, few efforts focused on the generation of IoT-specific datasets have also been seen in the literature recently. However, they are characterised by some limitations in terms of the IoT-specific information they include. For instance, the datasets proposed in [10, 11] are IoT-specific datasets but they lack of events reflecting attack scenarios. To address this limitation, the IoT-specific and network-related datasets proposed in [12, 13] contain events reflecting attack scenarios; however, they do not cover a diverse set of attack scenarios and do not include sensors' reading data or information related to the behaviour of the IoT/IIoT devices (e.g., sensors/actuators) within the network. Therefore, these IoT datasets can mainly be used for detecting only a limited number of network-based attacks against IoT/IIoT networks as they do not contain adequate information for detecting a wide range of network-based attacks and/or attacks that manipulate sensor measurement data or compromise IoT/IIoT devices within the IoT/IIoT network.