Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Intrusion detection is deemed to be a cornerstone of cyber security. Early and effective intrusion detection has been attracted much attention from researchers in the last decade. However, the existence of a deep and adequate study in using deep learning models for intrusion detection in cyber security is still seldom. In this study, I have investigated the problem of intrusion detection in three different environments, namely, personal computer, network and cloud computing. Furthermore, a double Particle Swarm Optimization-based algorithm is proposed for both feature and hyperparameter selection. Finally, a novel deep learning approach is presented to improve the performance of intrusion detection in cyber security area. 156 pp. Englisch. Nº de ref. del artículo: 9786200468161
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Librería: moluna, Greven, Alemania
Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Elmasry WisamWisam Elmasry received his B.Sc. and M.Sc. degrees in Computer Engineering from The Islamic University of Gaza (IUG), Palestine in 2004 and 2010, respectively. In 2019, he completed his doctorate in Computer Engineering . Nº de ref. del artículo: 497105463
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Librería: Books Puddle, New York, NY, Estados Unidos de America
Condición: New. Nº de ref. del artículo: 26400839095
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Librería: Majestic Books, Hounslow, Reino Unido
Condición: New. Print on Demand. Nº de ref. del artículo: 395570792
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Librería: Biblios, Frankfurt am main, HESSE, Alemania
Condición: New. PRINT ON DEMAND. Nº de ref. del artículo: 18400839101
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Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Intrusion detection is deemed to be a cornerstone of cyber security. Early and effective intrusion detection has been attracted much attention from researchers in the last decade. However, the existence of a deep and adequate study in using deep learning models for intrusion detection in cyber security is still seldom. In this study, I have investigated the problem of intrusion detection in three different environments, namely, personal computer, network and cloud computing. Furthermore, a double Particle Swarm Optimization-based algorithm is proposed for both feature and hyperparameter selection. Finally, a novel deep learning approach is presented to improve the performance of intrusion detection in cyber security area.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 156 pp. Englisch. Nº de ref. del artículo: 9786200468161
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Librería: preigu, Osnabrück, Alemania
Taschenbuch. Condición: Neu. Intrusion Detection Using Deep Learning | A Comprehensive Study in Cyber Security | Wisam Elmasry | Taschenbuch | Englisch | 2019 | LAP LAMBERT Academic Publishing | EAN 9786200468161 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand. Nº de ref. del artículo: 120469363
Cantidad disponible: 5 disponibles
Librería: AHA-BUCH GmbH, Einbeck, Alemania
Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Intrusion detection is deemed to be a cornerstone of cyber security. Early and effective intrusion detection has been attracted much attention from researchers in the last decade. However, the existence of a deep and adequate study in using deep learning models for intrusion detection in cyber security is still seldom. In this study, I have investigated the problem of intrusion detection in three different environments, namely, personal computer, network and cloud computing. Furthermore, a double Particle Swarm Optimization-based algorithm is proposed for both feature and hyperparameter selection. Finally, a novel deep learning approach is presented to improve the performance of intrusion detection in cyber security area. Nº de ref. del artículo: 9786200468161
Cantidad disponible: 1 disponibles