Optimized deep autoencoder and BiLSTM for intrusion detection in IoTs-Fog computing

Our world is rapidly evolving toward the Internet of Things (IoT), that connects all gadgets to digital services and simplifies our lives. As IoT devices expand, network vulnerabilities may rise, leading to more network threats. An intrusion detection system (IDS) with low latency and high accuracy...

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Bibliographische Detailangaben
Veröffentlicht in:	Multimedia tools and applications Jg. 84; H. 8; S. 4907 - 4943
1. Verfasser:	Alqahtani, Abdullah
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.03.2025 Springer Nature B.V
Schlagworte:	Accuracy Algorithms Computer Communication Networks Computer Science Computing time Data Structures and Information Theory Edge computing Error detection Internet of Things Intrusion detection systems Machine learning Multimedia Information Systems Network latency Optimization models Special Purpose and Application-Based Systems Workload Deep learning Multiple attack detection Fog computing Sparrow Search Optimization Algorithm Intrusion detection system Bidirectional LSTM Internet of Things Deep autoencoder Hyperparameter tuning
ISSN:	1573-7721, 1380-7501, 1573-7721
Online-Zugang:	Volltext
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