An End-to-End Framework for Machine Learning-Based Network Intrusion Detection System

An End-to-End Framework for Machine Learning-Based Network Intrusion Detection System

The increase of connected devices and the constantly evolving methods and techniques by attackers pose a challenge for network intrusion detection systems from conception to operation. As a result, we see a constant adoption of machine learning algorithms for network intrusion detection systems. How...

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Bibliographic Details
Main Authors: Gustavo De Carvalho Bertoli, Lourenco Alves Pereira Junior, Osamu Saotome, Aldri L. Dos Santos, Filipe Alves Neto Verri, Cesar Augusto Cavalheiro Marcondes, Sidnei Barbieri, Moises S. Rodrigues, Jose M. Parente De Oliveira
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Cybersecurity
datasets
intrusion detection system
machine learning
network security
supervised learning
Online Access:https://ieeexplore.ieee.org/document/9501960/

Internet

https://ieeexplore.ieee.org/document/9501960/

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