Data Analytics and Decision Support for Cybersecurity Trends, Method
The book illustrates the inter-relationship between several data management, analytics and decision support techniques and methods commonly adopted in Cybersecurity-oriented frameworks. The recent advent of Big Data paradigms and the use of data science m
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Iván Palomares Carrascosa Harsha Kumara Kalutarage Yan Huang Editors
Data Analytics and Decision Support for Cybersecurity
Data Analytics Series editors Longbing Cao, Advanced Analytics Institute, University of Technology, Sydney, Broadway, NSW, Australia Philip S. Yu, University of Illinois at Chicago, Chicago, IL, USA
Aims and Goals: Building and promoting the field of data science and analytics in terms of publishing work on theoretical foundations, algorithms and models, evaluation and experiments, applications and systems, case studies, and applied analytics in specific domains or on specific issues. Specific Topics: This series encourages proposals on cutting-edge science, technology and best practices in the following topics (but not limited to): Data analytics, data science, knowledge discovery, machine learning, big data, statistical and mathematical methods for data and applied analytics, New scientific findings and progress ranging from data capture, creation, storage, search, sharing, analysis, and visualization, Integration methods, best practices and typical examples across heterogeneous, interdependent complex resources and modals for real-time decision-making, collaboration, and value creation.
More information about this series at http://www.springer.com/series/15063
Iván Palomares Carrascosa Harsha Kumara Kalutarage • Yan Huang Editors
Data Analytics and Decision Support for Cybersecurity Trends, Methodologies and Applications
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Editors Iván Palomares Carrascosa University of Bristol Bristol, UK
Harsha Kumara Kalutarage Centre for Secure Information Technologies Queen’s University of Belfast Belfast, UK
Yan Huang Queen’s University Belfast Belfast, UK
ISSN 2520-1859 ISSN 2520-1867 (electronic) Data Analytics ISBN 978-3-319-59438-5 ISBN 978-3-319-59439-2 (eBook) DOI 10.1007/978-3-319-59439-2 Library of Congress Control Number: 2017946318 © Springer International Publishing AG 2017 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher
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