Addressing smart city security: machine and deep learning methodology combining feature selection and two-tier cooperative framework tuned by metaheuristics

The creation of safe and effective smart cities depends on the integration of internet of things (IoT) systems with cutting-edge technology. Conventional security techniques have not been able to keep up with the increasingly complex attacks targeting IoT networks. Adaptive artificial intelligence (...

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Veröffentlicht in:	Cluster computing Jg. 28; H. 11; S. 705
Hauptverfasser:	Zivkovic, Miodrag, Jovanovic, Luka, Jocovic, Vladimir, Nikolic, Bosko, Zeljkovic, Vico, Abdel-Salam, Mahmoud, Mravik, Milos, Muthusamy, Suresh, Bacanin, Nebojsa
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.10.2025 Springer Nature B.V
Schlagworte:	Accuracy Adaptive algorithms Algorithms Artificial intelligence Artificial neural networks Blockchain Cities Computer Communication Networks Computer Science Cybersecurity Datasets Deep learning Economic growth Evolutionary algorithms Explainable artificial intelligence Feature selection Heuristic methods Infrastructure Internet of Things Machine learning Methodology Operating Systems Optimization Processor Architectures Public services Security systems Smart cities Waste management AdaBoost Feature selection Smart cities LightGBM Firefly algorithm Security Optimization CNNs
ISSN:	1386-7857, 1573-7543
Online-Zugang:	Volltext
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