## 1 Introduction  
Recently, IoT technology has had a significant impact and is changing many different fields [1, 2]. The Industrial IoT (IIoT) paradigm has introduced a variety of innovations, including collaborative control systems and reliable and low-latency automated monitoring. The IoT has influenced many fields, including education [3], healthcare [4], transportation [5], industrial automation (high-reliability monitoring and control [6]), laboratory safety [7], and agriculture [8, 9]. In the healthcare sector, IoT devices have enabled regular patient monitoring. Various IoT systems have been employed in the transportation sector to detect and avoid accidents. By utilizing connected devices and real-time data analytics, these systems can monitor vehicle conditions, optimize routes, and provide drivers with timely alerts to prevent potential dangers [10, 11]. The number of IoT connections has increased significantly over the last ten years, and this development is expected to keep going across a wide range of industries. Furthermore, researchers have proposed various strategies to address potential challenges in IoT operations, leveraging scientific advancements in recent years [12, 13]. Despite these advantages, many challenges remain to be solved to provide efficient and safe operations, such as server technologies interoperability, standards, and security [14; 15]. The development and deployment of effective IoT applications involve new standards and requirements for the systems. In the case of the Internet of Vehicles (IoV), for example, it may be necessary to have quicker reaction times than the classic IoT applications required. The exponential growth in IoT connections over the past decade and projected future expansion necessitate robust security solutions. This increase in connection drives innovation in distributed infrastructure-based services, prompting research into mitigating potential operational challenges. Moreover, detecting and reducing attacks on IoT devices presents significant challenges due to different factors. For instance, identifying and reducing the impact of attacks could be made more difficult with distributed connections and light devices that do not have security features [16; 17]. Researchers have been working on datasets for attacks against IoT devices, but most fail to address multiple potential attacks and comprehensive network topology. The authors developed an extensive IoT attack dataset to address these issues. The dataset includes seven categories of attacks: spoofing, brute force, Web-based, Recon, DoS, DDoS, and Mirai, all carried out by malicious IoT devices that target other IoT devices [18]. Causal inference is the process of discovering whether and how a cause leads to an effect, generally utilizing statistical tools to distinguish between correlation and causation [19]. Extracting causal relationships from data is a significant task across diverse fields, including healthcare, drug discovery, online marketing, and e-commerce [20]. Consequently, a lot of studies have been carried out in the literature on statistics, computer science, and economics to develop algorithms and techniques for causal inference [21; 22]. Causal inference is an essential task in machine learning, aimed at understanding the fundamental mechanisms that determine the relationship between variables in a system [23]. Unlike standard forecasting modeling, which concentrates on finding statistical associations, causal inference attempts to find the true causal relationships that exist among the variables. This is crucial in domains where the goal is to understand the driving factors behind an outcome and to make informed decisions that can effectively influence the system [24]. The rapid growth and complexity of modern computer networks and the increasing sophistication of cyberattacks have led to an ever-increasing need for robust and reliable anomaly detection systems [25].