Cargando…
The Use of Ensemble Models for Multiple Class and Binary Class Classification for Improving Intrusion Detection Systems
The pursuit to spot abnormal behaviors in and out of a network system is what led to a system known as intrusion detection systems for soft computing besides many researchers have applied machine learning around this area. Obviously, a single classifier alone in the classifications seems impossible...
Autores principales: | Iwendi, Celestine, Khan, Suleman, Anajemba, Joseph Henry, Mittal, Mohit, Alenezi, Mamdouh, Alazab, Mamoun |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7249012/ https://www.ncbi.nlm.nih.gov/pubmed/32365937 http://dx.doi.org/10.3390/s20092559 |
Ejemplares similares
-
Realizing Efficient Security and Privacy in IoT Networks
por: Anajemba, Joseph Henry, et al.
Publicado: (2020) -
Analyzing the Effectiveness and Contribution of Each Axis of Tri-Axial Accelerometer Sensor for Accurate Activity Recognition
por: Javed, Abdul Rehman, et al.
Publicado: (2020) -
One-Class Classification by Ensembles of Random Planes (OCCERPs)
por: Ahmad, Amir
Publicado: (2022) -
A Lightweight Intelligent Network Intrusion Detection System Using One-Class Autoencoder and Ensemble Learning for IoT
por: Yao, Wenbin, et al.
Publicado: (2023) -
A multichannel EfficientNet deep learning-based stacking ensemble approach for lung disease detection using chest X-ray images
por: Ravi, Vinayakumar, et al.
Publicado: (2022)