## 2 Relevant Existing Contributions
### SCADA Architectures  
In this section, some of the most relevant existing contributions regarding SCADA Architecture are analytically enumerated. The contribution that is presented in [12] relates to an Intrusion Detection and Prevention System (IDPS), which is based on the Distributed Network Protocol 3 (DNP3). The proposed IDPS is called DIDEROT (Dnp3 Intrusion DetEction pReventiOn sysTem), and it uses both supervised Machine Learning (ML) and unsupervised/outlier ML detection models, which are able to infer whether a DNP3 network data flow is characteristic of a particular DNP3 cyberattack or anomaly. Thus, the supervised ML detection model is used, which attempts to assess whether a DNP3 network data flow is connected to a certain DNP3 cyberattack pattern. If the corresponding data pattern is deemed normal, then the unsupervised/outlier ML anomaly detection component is enabled, which aims to detect the presence of a possible problem. Considering the detection results of the DIDEROT system, the general Software Defined Networking (SDN) approach is chosen in order to efficiently mitigate the corresponding DNP3 cyberattacks and abnormal data traffic patterns. Nevertheless, the performance evaluation is not thoroughly conducted, which represents the main drawback of this paper. The authors of [51] describe an intrusion detection system (IDS) for DNP3 networks, which they claim is effective in order to observe and monitor critical data transfer operations in DNP3-based systems. The presented anomaly detection model considers possible attack patterns, which may bypass any rule-based deep packet inspection once the attackers obtain access to the main server systems. The first step shows that the data sets are generated, which mirror the DNP3 data traffic features, which are observed in real-world power grid substations for a sufficiently long time. Furthermore, the input features are extracted, which are determined by the function codes per TCP connection, along with other relevant TCP characteristics. Furthermore, an unsupervised deep learning model, called Autoencoder, is used to learn the normal behaviour of DNP3 traffic based on function code patterns. The authors refer to their approach as FC-AE-IDS (Function Code Autoencoder IDS). The proposed model is evaluated considering three datasets. The experimental results suggest that the proposed model's detection accuracy is approximately 95% in the case of all the attack scenarios that have been considered. The authors of [52] presented two relevant ideas. Thus, the first idea suggests the improvement of the SCADA architecture security using asymmetric cryptography models as well as digital signatures. The overhead that is generated is assessed from a quantitative perspective. This mediates reaching certain goals, such as the specification of data-origin authentication and also the traceability and implicit non-repudiation of commands given to the smart field and direct control equipment. Furthermore, the possibility of implementing digital signatures with a minimum impact on a standard and reliable industrial data communication protocol, such as the Distributed Network Protocol version 3 (DNP3), has been evaluated, and the obtained results are presented. Additionally, the second main idea of this paper regards the design and development of a multi-tenural cloud-based architecture, which is compatible with a SCADA environment. This architectural hypothesis is centred towards SCADA operators that manage multiple industrial control systems (ICS). This approach also has the goal of consolidating process data in a centralized fashion. The main contribution of [53] is to analyse the use of machine learning techniques comparatively in order to classify messages that conform to the same protocol, which are exchanged through encrypted tunnels. The study describes four simulated cases of encrypted DNP3 data traffic scenarios and also four different supervised machine learning algorithms. These are decision tree, nearest-neighbour, support vector machine, and naive Bayes.