Hierarchical reinforcement learning for efficient and effective automated penetration testing of large networks
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
-
1573-7675
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Anmerkungen
-
online resource.
- Erschienen in
-
Hierarchical reinforcement learning for efficient and effective automated penetration testing of large networks ; day:12 ; month:9 ; year:2022 ; pages:1-23
Journal of intelligent information systems ; (12.9.2022), 1-23
- Urheber
-
Ghanem, Mohamed C.
Chen, Thomas M.
Nepomuceno, Erivelton G.
- Beteiligte Personen und Organisationen
-
SpringerLink (Online service)
- DOI
-
10.1007/s10844-022-00738-0
- URN
-
urn:nbn:de:101:1-2022112921090608881030
- Rechteinformation
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
15.08.2525, 07:27 MESZ
Datenpartner
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Beteiligte
- Ghanem, Mohamed C.
- Chen, Thomas M.
- Nepomuceno, Erivelton G.
- SpringerLink (Online service)
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