Overview
- Provides a systematic assessment of the CSDS domain from organizational, methodological, and technical perspectives
- Frames key challenges facing the emerging CSDS profession, leading to structured best practice guidance
- Recommends focused approaches to instil greater empirical, theoretical, and scientific rigor in the security domain
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Table of contents (6 chapters)
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Front Matter
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Back Matter
Keywords
- cybersecurity
- data science
- CSDS
- security analytics
- data analytics
- machine learning
- Statistics
- Big Data
- data engineering
- data management
- MIS
- Design Science
- Practitioner Research
- analytics process
- unsupervised learning
- data scientist
- professionalization
- best practices
- analytics maturity
- cybersecurity maturity
About this book
This book encompasses a systematic exploration of Cybersecurity Data Science (CSDS) as an emerging profession, focusing on current versus idealized practice. This book also analyzes challenges facing the emerging CSDS profession, diagnoses key gaps, and prescribes treatments to facilitate advancement. Grounded in the management of information systems (MIS) discipline, insights derive from literature analysis and interviews with 50 global CSDS practitioners. CSDS as a diagnostic process grounded in the scientific method is emphasized throughout
Cybersecurity Data Science (CSDS) is a rapidly evolving discipline which applies data science methods to cybersecurity challenges. CSDS reflects the rising interest in applying data-focused statistical, analytical, and machine learning-driven methods to address growing security gaps. This book offers a systematic assessment of the developing domain. Advocacy is provided to strengthen professional rigor and best practices in the emerging CSDS profession.
This book will be of interest to a range of professionals associated with cybersecurity and data science, spanning practitioner, commercial, public sector, and academic domains. Best practices framed will be of interest to CSDS practitioners, security professionals, risk management stewards, and institutional stakeholders. Organizational and industry perspectives will be of interest to cybersecurity analysts, managers, planners, strategists, and regulators. Research professionals and academics are presented with a systematic analysis of the CSDS field, including an overview of the state of the art, a structured evaluation of key challenges, recommended best practices, and an extensive bibliography.
Authors and Affiliations
About the authors
Prof. dr. lr. Andrzej K. Hajdasinski (emeritus), PhD MSc, is a former fellow of the Technical University of Eindhoven (1976-77) and the Dutch Organization for Pure Scientific Research (ZWO) (1976-77). Formerly an Associate Professor in Systems Theory at Technical University of Eindhoven (1980-86), between 1986 and 2003 he worked with a range of IT consulting companies (a.o. Volmac Nederland, Cap Volmac, Pink Roccade and KPMG). From 2003 Hajdasinski has held the position of Business Development Executive with Capgemini Outsourcing B.V. During his professional career Hajdasinski has delivered 42 full scope industrial projects in the Netherlands, Germany, France, UK, Poland, USA, Finland, and Norway. He is an author of 30 international publications in renowned international journals, 25 conference papers, and has been a chairman of many international conferences.
Bibliographic Information
Book Title: Cybersecurity Data Science
Book Subtitle: Best Practices in an Emerging Profession
Authors: Scott Mongeau, Andrzej Hajdasinski
DOI: https://doi.org/10.1007/978-3-030-74896-8
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Nature Switzerland AG 2021
Hardcover ISBN: 978-3-030-74895-1Published: 03 October 2021
Softcover ISBN: 978-3-030-74898-2Published: 03 October 2022
eBook ISBN: 978-3-030-74896-8Published: 01 October 2021
Edition Number: 1
Number of Pages: XXVII, 388
Number of Illustrations: 99 b/w illustrations
Topics: Computer Science, general, IT in Business, Machine Learning