## 4 Traffic Analysis Methods
### Machine Learning Techniques for Traffic Classification
This method's advantages include a fast training phase and the fact that it is not easy to overfit due to the already mentioned large number of Decision Trees. The disadvantage is that it is unsuitable for low-dimensional and small datasets [36]. Logistic regression (LR) is a supervised learning technique. It is used for binary classification and uses general linear regression. The advantage of this method is the fast training phase and the possibility of dynamic adjustment of the classification threshold. The disadvantage of this method is easy overfitting [37]. AdaBoost is used to create a multi-classifier by integrating several weaker classifiers. By combining several classifiers and using their advantages together, the technique is able to achieve high classification accuracy. Additionally, there is no overfitting. However, the technique is sensitive to outliers [37]. Neural networks are based on the structure of the human nervous system. The basis of a neural network is a neuron or perceptron. These basic elements are interconnected and transmit signals to each other. Neurons form networks composed of layers. Networks are made up of an input layer, inner hidden layers, and an output layer. How inputs are converted to outputs depends on the value of weights, thresholds, transformation function, and network structure. The process in question is neural network learning. Neural network learning, as with machine learning, can be conducted with a teacher (supervised) or without a teacher (unsupervised). If a neural network has multiple layers, it is referred to as a Deep Neural Network (DNN). These algorithms include Convolutional Neural Networks (CNN) [41], Recurrent Neural Networks (RNN) [29], and Artificial Neural Networks (ANN) [16]. Table 2 makes a comparison of the most well-known and some of the most used ML approaches. These are mainly supervised approaches (this is due to the nature of having to perform partitioning into known, predefined classes). Each method has defined advantages and disadvantages. ML approaches represent an effective solution when classification needs to be performed, usually with sufficient recognition capabilities (metrics). The advantage over other approaches is the relative ease of use and the equally short time required to train the model. However, the individual results are strongly influenced by the chosen task/problem and equally strongly dependent on the chosen dataset (size, purity of records, etc.). The AdaBoost approach is deliberately not shown in the table, due to the fact that it is a combination of these approaches in order to achieve the highest quality results (metrics). Neural networks represent a more sophisticated approach that can achieve more quality metrics depending on the chosen task and, in particular, the quality of the dataset, thus creating a more robust model capable of representing more challenging structures. These approaches are well suited for large data volumes and more complex problems. However, this approach requires appropriate structure and individual parameter design (especially DNN approaches) and is also a more time and computationally intensive operation. Another advantage is that NN approaches are suitable for so-called transfer learning, where model \"learning\" and specific data (in this case industrial protocols) are performed. This results in a more robust model.