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Herausgeber: 
  • JYOTSNA KUMAR MANDAL
  • Anupam Ghosh
  • Rajdeep Chakraborty
  • Machine Learning Techniques and Analytics for Cloud Security 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   Auf Bestellung (Lieferzeit unbekannt)
    Veröffentlichung:  Januar 2022  
    Genre:  EDV / Informatik 
     
    Artificial Intelligence / Cloud Computing / computer science / Electrical & Electronics Engineering / Elektrotechnik u. Elektronik / Grid & Cloud Computing / Grid- u. Cloud-Computing / Informatik / Intelligent Systems & Agents / Intelligente Systeme u. Agenten / Künstliche Intelligenz / Maschinelles Lernen / Netzwerksicherheit
    ISBN:  9781119762256 
    EAN-Code: 
    9781119762256 
    Verlag:  Wiley 
    Einband:  Gebunden  
    Sprache:  English  
    Dimensionen:  H 264 mm / B 178 mm / D 30 mm 
    Gewicht:  988 gr 
    Seiten:  480 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    MACHINE LEARNING TECHNIQUES AND ANALYTICS FOR CLOUD SECURITY This book covers new methods, surveys, case studies, and policy with almost all machine learning techniques and analytics for cloud security solutions The aim of Machine Learning Techniques and Analytics for Cloud Security is to integrate machine learning approaches to meet various analytical issues in cloud security. Cloud security with ML has long-standing challenges that require methodological and theoretical handling. The conventional cryptography approach is less applied in resource-constrained devices. To solve these issues, the machine learning approach may be effectively used in providing security to the vast growing cloud environment. Machine learning algorithms can also be used to meet various cloud security issues, such as effective intrusion detection systems, zero-knowledge authentication systems, measures for passive attacks, protocols design, privacy system designs, applications, and many more. The book also contains case studies/projects outlining how to implement various security features using machine learning algorithms and analytics on existing cloud-based products in public, private and hybrid cloud respectively. Audience Research scholars and industry engineers in computer sciences, electrical and electronics engineering, machine learning, computer security, information technology, and cryptography.

      



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