Cargando…
Attack-Aware IoT Network Traffic Routing Leveraging Ensemble Learning
Network Intrusion Detection Systems (NIDSs) are indispensable defensive tools against various cyberattacks. Lightweight, multipurpose, and anomaly-based detection NIDSs employ several methods to build profiles for normal and malicious behaviors. In this paper, we design, implement, and evaluate the...
Autores principales: | Abu Al-Haija, Qasem, Al-Badawi, Ahmad |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8749547/ https://www.ncbi.nlm.nih.gov/pubmed/35009784 http://dx.doi.org/10.3390/s22010241 |
Ejemplares similares
-
Top-Down Machine Learning-Based Architecture for Cyberattacks Identification and Classification in IoT Communication Networks
por: Abu Al-Haija, Qasem
Publicado: (2022) -
Approach for Detecting Attacks on IoT Networks Based on Ensemble Feature Selection and Deep Learning Models
por: Rihan , Shaza Dawood Ahmed, et al.
Publicado: (2023) -
XRecon: An Explainbale IoT Reconnaissance Attack Detection System Based on Ensemble Learning
por: Alani, Mohammed M., et al.
Publicado: (2023) -
Leveraging Deep Learning for IoT Transceiver Identification
por: Gao, Jiayao, et al.
Publicado: (2023) -
A Spatiotemporal-Oriented Deep Ensemble Learning Model to Defend Link Flooding Attacks in IoT Network
por: Chen, Yen-Hung, et al.
Publicado: (2021)