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Guide to Vulnerability Analysis for Computer Networks and Systems

An Artificial Intelligence Approach

  • Simon Parkinson
  • Andrew Crampton
  • Richard Hill

Part of the Computer Communications and Networks book series (CCN)

Table of contents

  1. Front Matter
    Pages i-x
  2. Introduction and State-of-the-art

  3. Vulnerability Assessment Frameworks

    1. Front Matter
      Pages 57-57
    2. Kyle Coffey, Leandros A. Maglaras, Richard Smith, Helge Janicke, Mohamed Amine Ferrag, Abdelouahid Derhab et al.
      Pages 59-80
    3. Igor Kotenko, Elena Doynikova, Andrey Chechulin, Andrey Fedorchenko
      Pages 101-130
  4. Applications of Artificial Intelligence

    1. Front Matter
      Pages 157-157
    2. Vivin Paliath, Paulo Shakarian
      Pages 183-209
    3. Yi Shi, Yalin E. Sagduyu, Kemal Davaslioglu, Renato Levy
      Pages 211-234
    4. Muhammad Ajmal Azad, Junaid Arshad, Farhan Riaz
      Pages 235-258
    5. Deebiga Rajeswaran, Fabio Di Troia, Thomas H. Austin, Mark Stamp
      Pages 259-279
    6. Dhiviya Dhanasekar, Fabio Di Troia, Katerina Potika, Mark Stamp
      Pages 281-299
    7. Swathi Nambiar Kadala Manikoth, Fabio Di Troia, Mark Stamp
      Pages 301-315
    8. Liam Grant, Simon Parkinson
      Pages 317-335
  5. Visualisation

    1. Front Matter
      Pages 337-337
    2. Suvodeep Mazumdar, Jing Wang
      Pages 367-381
  6. Back Matter
    Pages 383-384

About this book

Introduction

This professional guide and reference examines the challenges of assessing security vulnerabilities in computing infrastructure. Various aspects of vulnerability assessment are covered in detail, including recent advancements in reducing the requirement for expert knowledge through novel applications of artificial intelligence. The work also offers a series of case studies on how to develop and perform vulnerability assessment techniques using start-of-the-art intelligent mechanisms.

Topics and features:

  • Provides tutorial activities and thought-provoking questions in each chapter, together with numerous case studies
  • Introduces the fundamentals of vulnerability assessment, and reviews the state of the art of research in this area
  • Discusses vulnerability assessment frameworks, including frameworks for industrial control and cloud systems
  • Examines a range of applications that make use of artificial intelligence to enhance the vulnerability assessment processes
  • Presents visualisation techniques that can be used to assist the vulnerability assessment process

In addition to serving the needs of security practitioners and researchers, this accessible volume is also ideal for students and instructors seeking a primer on artificial intelligence for vulnerability assessment, or a supplementary text for courses on computer security, networking, and artificial intelligence.

Dr. Simon Parkinson is a Senior Lecturer in Computer Science in the School of Computing and Engineering, University of Huddersfield, UK. Prof. Andrew Crampton is a Professor of Computational Mathematics in the School of Computing and Engineering, and the Associate Dean for Teaching and Learning at the University of Huddersfield. Prof. Richard Hill is a Professor of Intelligent Systems, the Head of the Department of Informatics, and the Director of the Centre for Industrial Analytics at the University of Huddersfield. His other publications include the successful Springer titles Guide to Security Assurance for Cloud ComputingBig-Data Analytics and Cloud ComputingGuide to Cloud Computing, and Cloud Computing for Enterprise Architectures.

Keywords

Vulnerability assessment Visualisation Anomaly and causality detection Identification of assets Data acquisition Knowledge extraction Mitigation techniques

Editors and affiliations

  1. 1.Department of Computer Science, School of Computing and EngineeringUniversity of HuddersfieldHuddersfieldUK
  2. 2.Department of Computer Science, School of Computing and EngineeringUniversity of HuddersfieldHuddersfieldUK
  3. 3.Department of Computer Science, School of Computing and EngineeringUniversity of HuddersfieldHuddersfieldUK

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-92624-7
  • Copyright Information Springer International Publishing AG, part of Springer Nature 2018
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-92623-0
  • Online ISBN 978-3-319-92624-7
  • Series Print ISSN 1617-7975
  • Series Online ISSN 2197-8433
  • Buy this book on publisher's site
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