Guide to Vulnerability Analysis for Computer Networks and Systems : An Artificial Intelligence Approach

Simon Parkinson

ISBN 10: 3319926233 / ISBN 13: 9783319926230

Published by Springer International Publishing, 2018

About this Item

Druck auf Anfrage Neuware - Printed after ordering - 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.

Seller Inventory # 9783319926230

Bibliographic Details

• Title: Guide to Vulnerability Analysis for Computer Networks and Systems : An Artificial Intelligence Approach
• Publisher: Springer International Publishing
• Publication Date: 2018
• Language: English
• ISBN 10: 3319926233
• ISBN 13: 9783319926230
• Binding: Buch
• Condition: Neu

About this title

Synopsis

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.

About the Author

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 Computing, Big-Data Analytics and Cloud Computing, Guide to Cloud Computing, and Cloud Computing for Enterprise Architectures.