## 2 Safety and Security  
Industrial IoT is a convergence area of Operational Technology (OT) and Information Technology (IT), both of which contribute to the safety and security issues of Industrial IoT. Security and safety is undoubtedly one of the most important aspects of IIoT. To underline this, the Industry IoT Consortium has published a technical report [20] about the security issues in IIoT systems, summarizing all the experience and knowledge of the consortium. The main goal in IIoT systems is reaching the trustworthy status, where trustworthiness \"is the degree of confidence one has that the system performs as expected in respect to all the key system characteristics in the face of environmental disruptions, human errors, system faults and attacks\". Figure 3 shows the key system characteristics of a trustworthy system when resisting external or internal threats. The key characteristics are [21]:  
**Security**: --Security ensures that the system is protected from unintended or unauthorized access, change, or destruction. **Privacy**: --Privacy provides organizations control over the collection, processing, and storage of their information, by deciding how this information can be shared both within their own organization and with others. **Reliability**: --Reliability guarantees that the system's operation is uninterrupted and error-free for the specified time. Availability is related to reliability, but also takes into account planned operation stops. **Safety**: --System Safety ensures that the people, property and environment are not at any unacceptable risk during the system's operation. **Resilience**: --System resilience provides a way to dynamically avoid, absorb and rapidly recover from changing adverse conditions. Resilience includes the ability to withstand and recover from deliberate attacks, accidents, or naturally occurring threats or incidents. While a secure and safe industrial IoT system requires appropriate system design, implementation and deployment, machine learning-based solutions are widely used to provide additional layers of security and safety. There are a couple of survey works that review the security issues of IIoT systems, mainly focusing on general security issues; however, machine learning-based solutions are presented as well in [1; 22]. In [23], a layer-wise analysis of security issues and solutions are shown, specifically, in 5G-based IIoT systems. Moreover, ref. [24] applies a layer-wise approach; besides information on common security issues, it provides an in-depth overview of the security aspects of edge and fog computing. While all the key characteristics of trustworthy IIoT systems are important, most of the machine learning-based solutions concern protection against unintended and unauthorized access (i.e., security). One of its typical application areas is _intrusion detection_ in which the learning method tries to detect unauthorized access to the system from arbitrary features, e.g., system logs, monitoring services, etc. To achieve this, most works use supervised learning techniques (i.e., classification) to detect intrusion from features, e.g., k-nearest neighbour (kNN) [25], support vector machines (SVM) [26; 27; 28], decision trees [29], Bayes networks [30; 31; 32], random forests [33] and also neural networks [34; 35; 36]. Besides common classification solutions, fuzzy methods and association-based methods can be found [37]. In the past, hidden Markov models (HMM) were also proposed [38; 39]. The subject of intrusion detection is so extensive that a couple of comprehensive survey papers focus on this topic alone [40; 41]. While classification is widely applied to intrusion detection, intrusion can be seen as an outlier against authorized users. The survey provided in [42] reviews the application areas of outlier detection in IoT systems, summarizing examples of intrusion detection in the field of IIoT.