## 3 Overview of IoT System Security Applications in the Industrial Field  
Safety has always been fundamental for industries and companies to protect operators and machines. With the advent of digitalization, which has opened new frontiers and opportunities, safety (now called cybersecurity) has taken on a new role: preventing cyber-attacks. Therefore, the introduction of the IoT has enabled the creation of different approaches to operator safety and cybersecurity [15]. With the introduction of the IoT, security in the industrial sector is divided into two macro areas: one for monitoring operator health and safety (industrial safety management, ISM) and the other for data privacy and protection. As part of IoT-based solutions, ISM is a crucial area with many development opportunities, especially regarding human safety. However, increasing industrial productivity and product quality is highly dependent on safety. The operators will be affected psychologically and physically if an accident occurs at work because of a safety procedure violation [52]. It also damages industry equipment and infrastructure, disrupting production. As consequence, workers lose confidence in their equipment and, at the same time, company owners suffer financial losses due to lost production time and industrial efficiency. As a result, companies stand to lose a lot from any violation's type of industrial safety regulations and standard operating procedures (SOPs) (Figure 7) [52]. Mobile technology advancements bring more intelligent and connected objects into our daily lives. The IoT is a movement that is becoming crucial to the networking and communications industry. However, weak security defaults, inconsistent communication and protocol standards, and update delivery issues have worsened cybersecurity threats to critical IoT equipment. Putting a heavy focus on securing the IoT devices in an intrusion detection system is a workable approach to lower risks and thwart these attacks [53]. [PERSON] et al. propose a clever system framework, highlighted in Figure 8, which uses digital twin (DT) and IoT technologies to achieve the real-time monitoring of workplace safety in the warehouse and ensure cyber-physical space synchronization for information traceability and visibility [54]. In this work, the authors have created an IoT-based workplace safety monitoring system (SOSMS) to find anomalous stationarity in cold rooms and acquire workers' spatiotemporal information in real time to help managers get a remote overall picture of the safety of personnel. The stacked auto-encoder (SAE) has an unsupervised deep neural structure intended to detect abnormal steady states in human movement, which may indicate an impending accident. The model's automated online updating mechanism is designed to work with calibration standards to keep up with the environment's changing state. Moreover, cyber-physical visibility and spatiotemporal traceability are improved by DT technology, which replicates actual things in cyberspace, enabling managers to implement emergency protocols and security monitoring efficiently. Figure 7: IoT for security applications in the industrial field: worker safety. For operator safety monitoring, [PERSON] et al. also addressed monitoring workers during mining operations [55]. They built an industrial IoT-enhanced intelligent crowd detection system for crowd monitoring for coal mine safety. Firstly, they proposed a particle swarm optimization-Elman neural network (PE) algorithm to predict the moving human pose. Secondly, they developed an ADI-LSTM algorithm, an absolute degree of incidence (ADI) and a long short-term memory (LSTM) neural network. Based on IoT-enhanced intelligent crowd detection that can predict the position of a moving human body and the pressure value exerted by machinery during mining through the pressure acting on the bearings, they evaluated the interaction between machines, people, and the environment to determine whether they were in a dangerous area.