Advanced Cybersecurity Framework for Detecting Fake Data Using Optimized Feature Selection and Stacked Ensemble Learning

As smart cities continue to generate vast quantities of data, data integrity is increasingly threatened by instances of fraud. Anomalous or fake data deteriorate the process and have impacts on decision-making systems and predictive analytics. Hence, an effective and intelligent fake data detection...

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Vydané v:	Electronics (Basel) Ročník 14; číslo 16; s. 3275
Hlavný autor:	Alajlan, Abrar M.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Basel MDPI AG 18.08.2025
Predmet:	Accuracy Algorithms Automation Classification Cybersecurity Cyberterrorism Data integrity Data security Datasets Decision making Decision trees Digital twins Discriminant analysis Ensemble learning Feature selection Fraud Information management Intrusion detection systems Learning strategies Machine learning Mathematical optimization Multilayer perceptrons Noise prediction Optimization Polar bear Polar bears Regression models Sensors Smart cities Traffic congestion Traffic flow
ISSN:	2079-9292, 2079-9292
On-line prístup:	Získať plný text
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