@article {10.3844/jcssp.2021.905.914,
article_type = {journal},
title = {Random Forests for Online Intrusion Detection in Computer Networks},
author = {Neto, Heitor Scalco and Lacerda, Wilian Soares and Françozo, Rafael Verão},
volume = {17},
number = {10},
year = {2021},
month = {Oct},
pages = {905-914},
doi = {10.3844/jcssp.2021.905.914},
url = {https://thescipub.com/abstract/jcssp.2021.905.914},
abstract = {This study proposes a methodology to build an Online Network Intrusion Detection System by using the Computational Intelligence technique called Random Forests and an API to preprocess the network packets. The experiments were carried out from two network traffic databases: The ISCX (i); and a test database (ii) created with the proposed API in our own network environment. The results obtained with the Random Forests technique show accuracy rates around 98%, bringing significant advances in the area of Intrusion Detection and affirming the high efficiency of the use of the technique to solve problems of intrusion detection in real network environments.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}