PRZEGLĄD GENERATYWNYCH SIECI PRZECIWSTAWNYCH DLA ZASTOSOWAŃ BEZPIECZEŃSTWA
Swarajya Madhuri Rayavarapu
madhurirayavarapu.rs@andhrauniversity.edu.inAndhra University, Department of Electronics and Communication Engineering (Indie)
https://orcid.org/0009-0007-7559-2142
Shanmukha Prasanthi Tammineni
Andhra University, Department of Electronics and Communication Engineering (Indie)
https://orcid.org/0009-0000-5352-2265
Sasibhushana Rao Gottapu
Andhra University, Department of Electronics and Communication Engineering (Indie)
Aruna Singam
Andhra University, Department of Electronics and Communication Engineering (Indie)
Abstrakt
Postępy w cyberbezpieczeństwie mają kluczowe znaczenie dla bezpieczeństwa gospodarczego i narodowego kraju. Ponieważ transmisja i przechowywanie danych gwałtownie rośnie, pilnie potrzebne są nowe techniki wykrywania i łagodzenia zagrożeń. Cyberbezpieczeństwo stało się absolutną koniecznością, ponieważ stale rosnąca liczba przesyłanych sieci z dnia na dzień powoduje wykładniczy wzrost danych przechowywanych na serwerach. Aby w przyszłości udaremnić wyrafinowane ataki, konieczna będzie regularna aktualizacja technik wykrywania zagrożeń i zabezpieczania danych. Generatywne sieci przeciwstawne (GAN) to klasa modeli uczenia maszynowego bez nadzoru, które mogą generować dane syntetyczne. Sieci GAN zyskują na znaczeniu w systemach cyberbezpieczeństwa opartych na sztucznej inteligencji do zastosowań takich jak wykrywanie włamań, steganografia, kryptografia i wykrywanie anomalii. W artykule dokonano kompleksowego przeglądu badań nad zastosowaniem sieci GAN do celów cyberbezpieczeństwa, w tym analizę popularnych zbiorów danych dotyczących cyberbezpieczeństwa oraz architektur modeli GAN wykorzystanych w tych badaniach.
Słowa kluczowe:
modele generatywne, cyberbezpieczeństwo, uczenie maszynowe, sieci neuronowe, uczenie się bez nadzoruBibliografia
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Autorzy
Swarajya Madhuri Rayavarapumadhurirayavarapu.rs@andhrauniversity.edu.in
Andhra University, Department of Electronics and Communication Engineering Indie
https://orcid.org/0009-0007-7559-2142
Autorzy
Shanmukha Prasanthi TammineniAndhra University, Department of Electronics and Communication Engineering Indie
https://orcid.org/0009-0000-5352-2265
Autorzy
Sasibhushana Rao GottapuAndhra University, Department of Electronics and Communication Engineering Indie
Autorzy
Aruna SingamAndhra University, Department of Electronics and Communication Engineering Indie
Statystyki
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