Schemes of combination of decision trees as a strategy for anomaly detection

Quintana-Zaez I.J.C.; Velarde-Bedregal H.R.; Anton-Vargas J.; Joaquim-Luis G. · Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology (2020) · DOI: 10.18687/laccei2020.1.1.306

Resumen

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.

Instituciones: Universidad Católica de Santa María