Cybersecurity Data Science
Springer International Publishing (Verlag)
978-3-030-74895-1 (ISBN)
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.
lt;p>
Scott Allen Mongeau, PhD MBA MA MA GD, OR Society CSci, INFORMS CAP, is a data analytics practitioner, researcher, and lecturer. His research is based in the management of information systems (MIS) domain, integrating organizational, process, and technical perspectives. He has over three decades of experience designing and deploying data analytics solutions across a range of industries. Currently an analytics solution engineer at Google, he has worked with Deloitte, SAS Institute, Genentech, and a range of companies as a consultant. Scott is based in Leiden, Netherlands.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.
1. Summary Introduction.- 2. Phase I: CSDS as an Emerging Profession - Diagnostic Literature Analysis.- 3 Phase II: CSDS Practitioners - Diagnostic Opinion Research and Gap Analysis.- 4 Phase III: CSDS Gap-Prescriptions - Design Science Problem Solving.- 5. Research Conclusions and Discussion.- 6. Managerial Recommendations.- References.
| Erscheinungsdatum | 15.08.2021 |
|---|---|
| Zusatzinfo | XXVII, 388 p. 99 illus. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 781 g |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| Mathematik / Informatik ► Mathematik ► Finanz- / Wirtschaftsmathematik | |
| Schlagworte | analytics maturity • analytics process • Best Practices • Big Data • CSDS • cybersecurity • cybersecurity maturity • data analytics • data engineering • Data Management • Data Science • Data Scientist • Design science • machine learning • MIS • Practitioner Research • professionalization • Security analytics • Statistics • Unsupervised Learning |
| ISBN-10 | 3-030-74895-2 / 3030748952 |
| ISBN-13 | 978-3-030-74895-1 / 9783030748951 |
| Zustand | Neuware |
| Haben Sie eine Frage zum Produkt? |
aus dem Bereich
