Optimized intrusion detection in IoT and fog computing using ensemble learning and advanced feature selection

The proliferation of Internet of Things (IoT) devices and fog computing architectures has introduced major security and cyber threats. Intrusion detection systems have become effective in monitoring network traffic and activities to identify anomalies that are indicative of attacks. However, constra...

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Vydáno v:	PloS one Ročník 19; číslo 8; s. e0304082
Hlavní autor:	Tawfik, Mohammed
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Public Library of Science 01.08.2024 Public Library of Science (PLoS)
Témata:	Access control Accuracy Algorithms Anomalies Benchmarks Biology and Life Sciences Cloud Computing Collaboration Communications traffic Computer and Information Sciences Computer Security Cybersecurity Cyberterrorism Data encryption Deep learning Detectors Earth Sciences Edge computing Engineering and Technology Ensemble learning Feature selection Infrastructure Internet of Things Intrusion detection systems IoT devices Machine Learning Neural Networks, Computer Nodes Optimization techniques Physical Sciences Traffic Traffic analysis Taiwan
ISSN:	1932-6203, 1932-6203
On-line přístup:	Získat plný text
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