Evaluation of the Performance of Unsupervised Learning Algorithms for Intrusion Detection in Unbalanced Data Environments

Evaluation of the Performance of Unsupervised Learning Algorithms for Intrusion Detection in Unbalanced Data Environments

This study evaluated the performance of unsupervised machine learning algorithms for intrusion detection in unbalanced data environments using the BoT-IoT dataset. Algorithms such as K-means++, DBSCAN, Local Outlier Factor (LOF), and Isolation Forest (I-forest) were analyzed using metrics like purit...

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Bibliographic Details
Main Authors: Gutierrez-Portela Fernando, Almenares Mendoza Florina, Calderon-Benavides Liliana
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Intrusion detection systems
unsupervised models
machine learning
anomaly detection
metrics
Online Access:https://ieeexplore.ieee.org/document/10794744/
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