## 2 Theoretical Fundamentals and State of the Art
However, the results may vary depending on the modeling method used and so the use of either a WB, GB or BB model has to be answered individually depending on the given circumstances and resources [56; 58]. Taking Machine Learning (ML) into account, the models can be developed into White Box Machine Learning Models (WBMLMs), Grey Box Machine Learning Models (GBMLMs), and Black Box Machine Learning Models (BBMLMs), as demonstrated in [52; 56]. In the case of SMEs, initial WBMs are especially important to consider, as there may not be enough experimental investigations nor resources to generate data from tests to meet the 5 Vs of Big Data, leading to an unsatisfactory result. In the further production process, data can be collected and fed into the initial WBM, which transforms it into a GBMLM, as shown in [56]. Large enterprises, on the other hand, may have already collected enough data to meet the 5 Vs of Big Data and can implement a BBM that is also fed with data collected in the process, transforming it into a BBMLM [52]. Nevertheless, in the event of limited access to sufficient data, a similar approach to SMEs can be adopted. Data that can be used by the models has to be classified and, in the case of an ML approach, supervised learning is recommended [23; 54]. To obtain the necessary data in the required quality, time and structure, IIoT solutions, despite classical level 2 automation schemes, are required to realize a true BBM or GBM as well as their further advanced ML-based extensions. IIoT is a derivative of the IoT, which describes the attempt to network smart devices across the board, whereby this term is strongly consumer-related [59; 60]. The architecture associated with IoT has to be adopted when implemented in the context of an industrial environment, especially considering higher IT security and resilience requirements [60]. As concluded by [60], IIoT can be described as a superordinate system including connected cyber-physical entities that enable in-situ data acquisition, analysis, and exchange in an industrial environment leading to process and production optimization and thus serving as a major enabler for a leaner production [60; 61]. The resulting benefits are improved productivity and efficiency, reduced cost and energy consumption as well as a strengthened customer relationship [59; 60; 62]. A major challenge arises from the heterogeneous application of protocols [2; 60]. The general term protocols can be divided into data protocols (e.g., XMPP, MQTT), discovery protocols (e.g., mDNS) and infrastructure protocols (e.g., IPv4, IPv6), enabling communication, whereby each of them exhibits their individual advantages as well as disadvantages [60]. As stated by [2; 60], Message Queue Telemetry Transport (MQTT) offers itself for industrial use due to efficient data storage. Furthermore, [2] pointed out the suitability of Extensible Messaging and Presence Protocol (XMPP) for HMIs, which serve as a key component in a smart factory. The integration of a large number of smart devices into the IIoT also poses several risks in terms of IT-security and makes production sites particularly vulnerable to cyberattacks [63; 64]. A smart factory, consisting of a multitude of cyber-physical entities, offers attack points in the areas of software (viruses, trojans), protocols (man-in-the-middle, denial-of-service), and hardware [63; 64]. This can not only paralyze production but also lead to data theft or targeted manipulation of processes [32; 59]. As stated by [32], WB(ML)Ms, GB(ML)Ms, and BB(ML)Ms, when connected, e.g., as a DS or DT, can also be impacted by cyberattacks due to manipulation of the general model, underlying ML algorithm or related data sets. To be able to reduce or at best completely avert cyberattacks and the associated potential intellectual and physical damage, the involvement of security experts should be considered in any case when implementing a smart factory.