Guide to Vulnerability Analysis for Computer Networks and Systems
An Artificial Intelligence Approach
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Beschreibung
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 Computing, Big-Data Analytics and Cloud Computing, Guide to Cloud Computing, and Cloud Computing for Enterprise Architectures.
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.
Highlights the strong requirement for expert knowledge, and how a lack of this is preventing businesses from performing their own vulnerability assessments Approaches the challenges of IT vulnerability assessment from both technical and business perspectives, taking into consideration the latest IT trends Helps the reader to improve their understanding of the challenges and issues covered, through case studies suitable for readers of different abilities (e.g., both students and practitioners) Proposes solutions that utilise artificial intelligence to develop rich functionality whilst minimising the need for end-user expert knowledge
Autor*in
Simon Parkinson
Themen in »Guide to Vulnerability Analysis for Computer Networks and Systems«
Vulnerability assessment Visualisation Anomaly and causality detection Identification of assets Data acquisition Knowledge extraction Mitigation techniques