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Uczenie federacyjne dla Internetu pojazdów

Uczenie federacyjne dla Internetu pojazdów

Inès BEN JAAFAR / Moheddine Rabaoui

95,78 €
IVA incluido
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Editorial:
KS OmniScriptum Publishing
Año de edición:
2025
Materia
Informática: cuestiones generales
ISBN:
9786208801601
95,78 €
IVA incluido
Disponible

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Sobre IBD

Szybka ewolucja Internetu Pojazdów (IoV) wprowadza znaczące postępy w inteligentnych systemach transportowych, ale także stawia krytyczne wyzwania w zakresie bezpieczeństwa danych, prywatności i podejmowania decyzji w czasie rzeczywistym. W niniejszym badaniu zaproponowano oparte na uczeniu federacyjnym (FL) ramy bezpieczeństwa dla IoV, integrujące uśrednianie federacyjne (FedAvg) i prywatność różnicową (DP) w celu zwiększenia cyberbezpieczeństwa przy jednoczesnym zachowaniu prywatności danych. Proponowany model wykorzystuje zdecentralizowane techniki uczenia maszynowego w celu złagodzenia zagrożeń bezpieczeństwa, zmniejszenia zależności od transmisji nieprzetworzonych danych i zapobiegania nieautoryzowanemu dostępowi do wrażliwych danych pojazdu i użytkownika. Poprzez szeroko zakrojoną analizę empiryczną z wykorzystaniem rzeczywistych zbiorów danych dotyczących cyberbezpieczeństwa, badania te oceniają wydajność, skalowalność i skuteczność mechanizmów bezpieczeństwa opartych na FL w porównaniu z konwencjonalnymi podejściami.

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