to use traditional methods, which include classification based on ports used, or more sophisticated approaches using Artificial Intelligence (AI). Using such approaches, it is possible to perform an in-depth analysis of the monitored data stream (or other data units) and classify not only the protocol used but also, for example, the cipher suite used. Performing classification in OT networks is currently less common, but it provides great potential in terms of security benefits for such networks. It is for this reason that this paper has been created, in order to describe and summarize the different protocol classification methods (especially in OT networks) and also the available datasets.  
This paper focuses on the protocol recognition aspects of OT networks. Advanced methods using AI techniques can be used to perform protocol recognition with additional recognition capability. Conventional techniques, such as relying on known ports, may not be fully sufficient and thus more advanced techniques that are able to directly detect/recognize the protocol itself (trained marks of the protocol) need to be employed. Based on the analysis of the current state of the art, it is clear that the recognition of industrial protocols is rather minor, as is the current state of publicly available datasets. Thus, this paper points out this gap (research gap), and for this reason, it performs (i) a summarization of methods for network traffic classification, (ii) a summarization of methods for recognition and identification of the protocol used in the network, (iii) the use of machine learning methods for industrial protocol recognition. Finally, (iv) an analysis of publicly available datasets that can be used for industrial protocol classification was performed. This article takes aim at the scientific question: How can industrial protocol classification be achieved? What publicly available datasets can currently be used specifically for the purpose of classifying these protocols? OT networks require increased attention due to IT and OT convergence, in particular, due to the increased risk of cyber-attacks that may target these networks. Convergence has caused, among other things, a proliferation of attack vectors, making advanced data monitoring necessary and using a software-as-a-service (SaaS) approach. Industrial protocol classification thus enables (i) automatic detection of the protocol used to assess security, including the cipher suite used, (ii) diagnostic data, network monitoring (protocol usage within different sectors, etc.), (iii) automation of audit tools, and (iv) development of protocol adaptive solutions--automatic protocol detection and further actions following this knowledge.  
The structure of this paper is as follows: Section 2 describes the specifics of OT networks, the effects of the convergence of IT and OT networks, and, hence, the need to use sophisticated methods to enhance security in the OT industry. Section 3 presents an analysis and comparison of the current state of the art, focusing mainly on the issues of protocol recognition and traffic classification. Furthermore, Section 4 presents various methods for the purpose of traffic analysis. The section presents approaches to protocol classification, recognition and identification, Machine Learning (ML) methods, and metrics used to evaluate models. Section 5 focuses on the available datasets usable for the purpose of traffic classification and the chosen protocol, and a comparison of the most relevant datasets in terms of several parameters is also provided.