Isbn: 9786207456727 - hybrid machine learning and meta-heuristic algorithms for ddos attack: theory and case study (6 risultati)

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      Lingua: Inglese

      Editore: LAP LAMBERT Academic Publishing, 2024

      6207456726 / 9786207456727

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      Taschenbuch. Condizione: Neu. Hybrid Machine Learning and Meta-Heuristic Algorithms for DDOS Attack | Theory and Case Study | Sumathi S (u. a.) | Taschenbuch | Englisch | 2024 | LAP LAMBERT Academic Publishing | EAN 9786207456727 | Verantwortliche Person für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu.

    • Lingua: Inglese

      Editore: LAP Lambert Academic Publishing, 2024

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      paperback. Condizione: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

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      Editore: LAP LAMBERT Academic Publishing Jan 2024, 2024

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      Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.

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      Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 188 pp. Englisch.

    • Lingua: Inglese

      Editore: LAP Lambert Academic Publishing

      6207456726 / 9786207456727

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      Da: moluna, Greven, Germaniamoluna

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      Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. The Distributed Denial of Service (DDoS) attack is a kind of intrusion in a cloud computing environment that severely affects the end user by injecting illegitimate packets of data into internet traffic without the knowledge of the clients. It is a serious .

    • Lingua: Inglese

      Editore: LAP LAMBERT Academic Publishing Jan 2024, 2024

      6207456726 / 9786207456727

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      Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000

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      Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The Distributed Denial of Service (DDoS) attack is a kind of intrusion in a cloud computing environment that severely affects the end user by injecting illegitimate packets of data into internet traffic without the knowledge of the clients. It is a serious problem in cloud computing because the detection and mitigation of intrusion is a challenging task that will affect the functionality of the entire architecture. Numerous cyber-security measures have been carried out to protect the server from attackers or hackers. The traditional cyber-security methods failed to protect the server against several external unauthorized traffic. It is important to develop an Intrusion Detection System (IDS) in loT architecture. This book aims to provide detailed literature reviews carried out to investigate various machine learning techniques, neural network models, and optimization algorithms aimed to identify the gap problems and then develop machine learning algorithms to detect the intrusion accurately and effectively.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 188 pp. Englisch.

    • Lingua: Inglese

      Editore: LAP LAMBERT Academic Publishing

      6207456726 / 9786207456727

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      Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH

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      EUR 113,15

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      Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The Distributed Denial of Service (DDoS) attack is a kind of intrusion in a cloud computing environment that severely affects the end user by injecting illegitimate packets of data into internet traffic without the knowledge of the clients. It is a serious problem in cloud computing because the detection and mitigation of intrusion is a challenging task that will affect the functionality of the entire architecture. Numerous cyber-security measures have been carried out to protect the server from attackers or hackers. The traditional cyber-security methods failed to protect the server against several external unauthorized traffic. It is important to develop an Intrusion Detection System (IDS) in loT architecture. This book aims to provide detailed literature reviews carried out to investigate various machine learning techniques, neural network models, and optimization algorithms aimed to identify the gap problems and then develop machine learning algorithms to detect the intrusion accurately and effectively.