## 4 Conclusions  
This work presented a computational scheme for DGA classification, composed of a deep learning model based on deep convolutional neural network and natural language processing and a technique for class increment by transfer learning to allow the increment of a new DGA family to the multiclass classifier model, avoiding catastrophic forgetting and maintaining the levels of the class metrics already known by the model. First, observing the results presented in Section 3, it is possible to see that the proposed Model 1 obtained metrics very well aligned with the state-of-the-art research related to the multiclass classification of DGA, including obtaining metrics superior to some models obtained from the literature in direct comparison, using the same dataset for training and testing. These results reinforce the robustness of the work presented and its positioning in recent literature for the classification of DGA. The results obtained by the proposed Model 1 were also superior, on average, by around 19.5% to the results obtained by Model 2, a modified model, without the embedding layer in the data input, thus demonstrating the impact of applying character-level embedding on the results of the models presented. Regarding the class increment technique, it was noticed that, in general, the number of examples available for the new class that should be added to the model does not have a significant impact on the general metrics after applying the class increment technique by transfer learning, since the results obtained by the three families tested as an incremental class with different numbers of examples in the dataset were very close. It is also possible to verify that the proposed class increment technique proved to be efficient, keeping the levels of the model's metrics very close to those obtained at the time of initial training, and also much higher than the metrics obtained by the model trained exclusively with the reduced dataset, which reinforces the thesis that there was transfer of learning during the class increase process. Likewise, it can be concluded that the presented technique proved to be efficient in avoiding catastrophic forgetting in the incremental training of the proposed model for classifying examples from 61 DGA families. Thus, the proposed computational scheme, combining the proposed deep learning model with the presented class increment technique, proved to be efficient both in classifying DGA families and robust and reliable in incrementing a new family to the multiclass classifier, avoiding catastrophic forgetting satisfactorily and keeping metrics balanced and at high levels. For future work, the application of deep neural network techniques and models to seek better results in DGA multiclass classification can be suggested, since the general metrics are not yet at levels close to the limit, performing at around 80%. For example, the application of Large Language Models (LLMs), which have presented outstanding results in the area of Natural Language Processing (NLP). Possibly, Graph Neural Networks (GNNs) could be an innovative alternative to searching for relationships (edges) between domain names (vertices) of the same family. It would also be interesting to apply unsupervised learning techniques in order to find the occurrence of new DGA families in an automated way. This work presented results obtained with the increment of a class to a previously trained model. It may be interesting to evaluate the degradation of the metrics with the increment of more than one class. The integration of classifier models in real-world environments, through Passive DNS, would also be an interesting initiative, in order to observe and validate the performance of artificial intelligence models in a real environment. Conceptualization, J.R.G. and A.M.C. ; methodology, J.R.G. ; software, J.R.G. ; validation, J.R.G., A.M.C. and L.A.N. ; formal analysis, J.R.G., A.M.C. and L.A.N. ; investigation, J.R.G. and A.M.C. ; resources, J.R.G., A.M.C. and L.A.N. ; data curation, J.R.G.