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Machine Intelligence and Big Data Analytics for Cybersecurity Applications

  • Book
  • © 2021

Overview

Editors:
  1. Yassine Maleh

    1. Sultan Moulay Slimane University, Beni Mellal, Morocco

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  2. Mohammad Shojafar

    1. Institute for Communication Systems, University of Surrey, Guildford, UK

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  3. Mamoun Alazab

    1. Charles Darwin University, Darwin, Australia

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  4. Youssef Baddi

    1. Chouaib Doukkali University El Jadida, El Jadida, Morocco

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  • Presents the latest discoveries in terms of machine intelligence and Big data analytics techniques and methods for cybersecurity and privacy
  • Proposes many case studies and applications of machine intelligence in various cybersecurity fields (Smart City, IoT, Cyber Physical System, etc.)
  • Combines theory and practice so that readers can find both a description of the concepts and context related to machine intelligence for cybersecurity

Part of the book series: Studies in Computational Intelligence (SCI, volume 919)

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Table of contents (24 chapters)

  1. Front Matter

    Pages i-xiii
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  2. Machine Intelligence and Big Data Analytics for Cybersecurity: Fundamentals and Challenges

    1. Front Matter

      Pages 1-1
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    2. Network Intrusion Detection: Taxonomy and Machine Learning Applications

      • Anjum Nazir, Rizwan Ahmed Khan
      Pages 3-28

    3. Machine Learning and Deep Learning Models for Big Data Issues

      • Youssef Gahi, Imane El Alaoui
      Pages 29-49

    4. The Fundamentals and Potential for Cybersecurity of Big Data in the Modern World

      • Reinaldo Padilha França, Ana Carolina Borges Monteiro, Rangel Arthur, Yuzo Iano
      Pages 51-73

    5. Toward a Knowledge-Based Model to Fight Against Cybercrime Within Big Data Environments: A Set of Key Questions to Introduce the Topic

      • Mustapha El Hamzaoui, Faycal Bensalah
      Pages 75-101

  3. Machine Intelligence and Big Data Analytics for Cyber-Threat Detection and Analysis

    1. Front Matter

      Pages 103-103
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    2. Improving Cyber-Threat Detection by Moving the Boundary Around the Normal Samples

      • Giuseppina Andresini, Annalisa Appice, Francesco Paolo Caforio, Donato Malerba
      Pages 105-127

    3. Bayesian Networks for Online Cybersecurity Threat Detection

      • Mauro José Pappaterra, Francesco Flammini
      Pages 129-159

    4. Spam Emails Detection Based on Distributed Word Embedding with Deep Learning

      • Sriram Srinivasan, Vinayakumar Ravi, Mamoun Alazab, Simran Ketha, Ala’ M. Al-Zoubi, Soman Kotti Padannayil
      Pages 161-189

    5. AndroShow: A Large Scale Investigation to Identify the Pattern of Obfuscated Android Malware

      • Md. Omar Faruque Khan Russel, Sheikh Shah Mohammad Motiur Rahman, Mamoun Alazab
      Pages 191-216

    6. IntAnti-Phish: An Intelligent Anti-Phishing Framework Using Backpropagation Neural Network

      • Sheikh Shah Mohammad Motiur Rahman, Lakshman Gope, Takia Islam, Mamoun Alazab
      Pages 217-230

    7. Network Intrusion Detection for TCP/IP Packets with Machine Learning Techniques

      • Hossain Shahriar, Sravya Nimmagadda
      Pages 231-247

    8. Developing a Blockchain-Based and Distributed Database-Oriented Multi-malware Detection Engine

      • Sumit Gupta, Parag Thakur, Kamalesh Biswas, Satyajeet Kumar, Aman Pratap Singh
      Pages 249-275

    9. Ameliorated Face and Iris Recognition Using Deep Convolutional Networks

      • Balaji Muthazhagan, Suriya Sundaramoorthy
      Pages 277-296

    10. Presentation Attack Detection Framework

      • Hossain Shahriar, Laeticia Etienne
      Pages 297-311

    11. Classifying Common Vulnerabilities and Exposures Database Using Text Mining and Graph Theoretical Analysis

      • Ferda Özdemir Sönmez
      Pages 313-338

  4. Machine Intelligence and Big Data Analytics for Cybersecurity Applications

    1. Front Matter

      Pages 339-339
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    2. A Novel Deep Learning Model to Secure Internet of Things in Healthcare

      • Usman Ahmad, Hong Song, Awais Bilal, Shahid Mahmood, Mamoun Alazab, Alireza Jolfaei et al.
      Pages 341-353

    3. Secure Data Sharing Framework Based on Supervised Machine Learning Detection System for Future SDN-Based Networks

      • Anass Sebbar, Karim Zkik, Youssef Baddi, Mohammed Boulmalf, Mohamed Dafir Ech-Cherif El Kettani
      Pages 355-371
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Keywords

About this book

This book presents the latest advances in machine intelligence and big data analytics to improve early warning of cyber-attacks, for cybersecurity intrusion detection and monitoring, and malware analysis. Cyber-attacks have posed real and wide-ranging threats for the information society. Detecting cyber-attacks becomes a challenge, not only because of the sophistication of attacks but also because of the large scale and complex nature of today’s IT infrastructures. It discusses novel trends and achievements in machine intelligence and their role in the development of secure systems and identifies open and future research issues related to the application of machine intelligence in the cybersecurity field. Bridging an important gap between machine intelligence, big data, and cybersecurity communities, it aspires to provide a relevant reference for students, researchers, engineers, and professionals working in this area or those interested in grasping its diverse facets and exploringthe latest advances on machine intelligence and big data analytics for cybersecurity applications.

 

 

Editors and Affiliations

  • Sultan Moulay Slimane University, Beni Mellal, Morocco

    Yassine Maleh

  • Institute for Communication Systems, University of Surrey, Guildford, UK

    Mohammad Shojafar

  • Charles Darwin University, Darwin, Australia

    Mamoun Alazab

  • Chouaib Doukkali University El Jadida, El Jadida, Morocco

    Youssef Baddi

Bibliographic Information

  • Book Title: Machine Intelligence and Big Data Analytics for Cybersecurity Applications

  • Editors: Yassine Maleh, Mohammad Shojafar, Mamoun Alazab, Youssef Baddi

  • Series Title: Studies in Computational Intelligence

  • DOI: https://doi.org/10.1007/978-3-030-57024-8

  • Publisher: Springer Cham

  • eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021

  • Hardcover ISBN: 978-3-030-57023-1Published: 15 December 2020

  • Softcover ISBN: 978-3-030-57026-2Published: 16 December 2021

  • eBook ISBN: 978-3-030-57024-8Published: 14 December 2020

  • Series ISSN: 1860-949X

  • Series E-ISSN: 1860-9503

  • Edition Number: 1

  • Number of Pages: XIII, 539

  • Number of Illustrations: 38 b/w illustrations, 172 illustrations in colour

  • Topics: Data Engineering, Computational Intelligence, Machine Learning

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