Machine learning-based network intrusion detection for big and imbalanced data using oversampling, stacking feature embedding and feature extraction

Machine learning-based network intrusion detection for big and imbalanced data using oversampling, stacking feature embedding and feature extraction

Abstract Cybersecurity has emerged as a critical global concern. Intrusion Detection Systems (IDS) play a critical role in protecting interconnected networks by detecting malicious actors and activities. Machine Learning (ML)-based behavior analysis within the IDS has considerable potential for dete...

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Bibliographic Details
Main Authors: Md. Alamin Talukder, Md. Manowarul Islam, Md Ashraf Uddin, Khondokar Fida Hasan, Selina Sharmin, Salem A. Alyami, Mohammad Ali Moni
Format: Article
Language:English
Published: SpringerOpen 2024-02-01
Series:Journal of Big Data
Subjects:
Intrusion detection system
Feature extraction
Random oversampling
Principal component analysis
Machine learning
Online Access:https://doi.org/10.1186/s40537-024-00886-w

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