IoT Intrusion Detection Using Machine Learning with a Novel High Performing Feature Selection Method

IoT Intrusion Detection Using Machine Learning with a Novel High Performing Feature Selection Method

The Internet of Things (IoT) ecosystem has experienced significant growth in data traffic and consequently high dimensionality. Intrusion Detection Systems (IDSs) are essential self-protective tools against various cyber-attacks. However, IoT IDS systems face significant challenges due to functional...

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Bibliographic Details
Main Authors: Khalid Albulayhi, Qasem Abu Al-Haija, Suliman A. Alsuhibany, Ananth A. Jillepalli, Mohammad Ashrafuzzaman, Frederick T. Sheldon
Format: Article
Language:English
Published: MDPI AG 2022-05-01
Series:Applied Sciences
Subjects:
cybersecurity
anomaly detection accuracy
feature selection
Internet of Things (IoT)
intrusion detection system
and machine learning
Online Access:https://www.mdpi.com/2076-3417/12/10/5015

Internet

https://www.mdpi.com/2076-3417/12/10/5015

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