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Guide to Vulnerability Analysis for Computer Networks and Systems [electronic resource] : An Artificial Intelligence Approach / edited by Simon Parkinson, Andrew Crampton, Richard Hill.

Publication:
Cham : Springer International Publishing : Imprint: Springer, 2018.
Format/Description:
Book
1 online resource (X, 384 pages) : 117 illustrations, 103 illustrations in color.
Edition:
1st ed. 2018.
Series:
Computer Science (Springer-11645)
Computer communications and networks 1617-7975
Computer Communications and Networks, 1617-7975
Contained In:
Springer eBooks
Status/Location:
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Details

Subjects:
Computer networks.
Computer security.
Computer software-Reusability.
Computers.
Cognitive psychology.
Local subjects:
Computer Communication Networks. (search)
Systems and Data Security. (search)
Performance and Reliability. (search)
Information Systems and Communication Service. (search)
Cognitive Psychology. (search)
System Details:
text file PDF
Summary:
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.
Contents:
Part I: Introduction and State of the Art
Review of the State of the Art of Vulnerability Assessment Using Artificial Intelligence
A Survey of Machine Learning Algorithms and Their Application in Information Security
Part II: Vulnerability Assessment Frameworks
Vulnerability Assessment of Cybersecurity for SCADA Systems
A Predictive Model for Risk and Trust Assessment in Cloud Computing: Taxonomy and Analysis for Attack Pattern Detection
AI and Metrics-Based Vulnerability-Centric Cyber Security Assessment and Countermeasure Selection
Artificial Intelligence Agents as Mediators of Trustless Security Systems and Distributed Computing Applications
Part III: Applications of Artificial Intelligence
Automated Planning of Administrative Tasks Using Historic Events: a File System Case Study
Defending Against Chained Cyber-Attacks by Adversarial Agents
Vulnerability Detection and Analysis in Adversarial Deep Learning
SOCIO-LENS: Spotting Unsolicited Callers Through Network Analysis
Function Call Graphs Versus Machine Learning for Malware Detection
Detecting Encrypted and Polymorphic Malware Using Hidden Markov Models
Masquerade Detection on Mobile Devices
Identifying File Interaction Patterns in Ransomware Behaviour
Part IV: Visualisation
A Framework for the Visualisation of Cyber Security Requirements and its Application in BPMN
Big Data and Cyber Security: A Visual Analytics Perspective.
Contributor:
Parkinson, Simon, editor., Editor,
Crampton, Andrew, editor., Editor,
Hill, Richard, editor., Editor,
SpringerLink (Online service)
Other format:
Printed edition:
Printed edition:
Printed edition:
ISBN:
978-3-319-92624-7
9783319926247
9783319926230 (Printed edition)
9783319926254 (Printed edition)
9783030064747 (Printed edition)
Publisher Number:
10.1007/978-3-319-92624-7 doi
Access Restriction:
Restricted for use by site license.