Enhanced Anomaly Detection in IoT Networks Using Deep Autoencoders with Feature Selection Techniques

An enormous number of the Internet of Things (IoT) applications and their networks have significantly impacted people’s lives in diverse situations. With the increasing adoption of these applications in various sectors, ensuring reliability and security has become a critical concern. Moreover, the n...

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Veröffentlicht in:	Sensors (Basel, Switzerland) Jg. 25; H. 10; S. 3150
Hauptverfasser:	Rhachi, Hamza, Balboul, Younes, Bouayad, Anas
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Switzerland MDPI AG 16.05.2025 MDPI
Schlagworte:	Algorithms anomaly detection Artificial intelligence Comparative analysis Cybersecurity data pre-processing Data security dataset Datasets deep autoencoder Deep learning Energy consumption Feature selection Internet of Things Literature reviews Machine learning Neural networks Research methodology Wireless networks Morocco internet of things data pre-processing anomaly detection deep autoencoder classification dataset feature selection
ISSN:	1424-8220, 1424-8220
Online-Zugang:	Volltext
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