Esquemas de combinación de árboles de decisión como estrategia para la detección de anomalías

Abstract

Security of the data consumed, generated and stored is crucial to the quality of life in today's society. For this reason, this paper proposes a comparative study of different combination schemes of multiple classifiers based on decision trees, due to its scalability and easy implementation. As a result, precision and recall values of about 97% and 100% were obtained, showing their high reliability, reducing false alarms and high generalization capacity. A comparison with a deep learning based algorithm showed that tree combination strategies are competitive and with statistically similar and superior results to the state-of-the-art. In the end, the results suggest that adaptive strategies such as XGBoost or highly randomized strategies such as Random Forest or Extra-Tree can be alternatives for the protection of precious data on the network.