Overview
- Guides cyber-forensic researchers, educators, and practitioners through the process of conducting database forensics
- Describes a practical forensic process to acquire and analyze databases from a given device and/or application
- Reinforces the importance of keeping up to date on the cyber-threat landscape
Part of the book series: Studies in Big Data (SBD, volume 116)
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Table of contents (8 chapters)
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Front Matter
Keywords
About this book
Adopting an experimental learning approach, this book describes a practical forensic process to acquire and analyze databases from a given device and/or application. Databases hold important, sensitive, and/or confidential information and are a crucial source of evidence in any digital investigation. This also reinforces the importance of keeping up to date on the cyber-threat landscape as well as any associated database forensic challenges and approaches. The book also guides cyber-forensic researchers, educators, and practitioners through the process of conducting database forensics and investigations on mobile devices, Internet of Things (IoT) devices, web browsers, and end-to-end encrypted instant messaging applications. Given the fast-changing database forensics landscape, this book will be of interest to researchers, educators, and practitioners in the field, as well as students who want to learn about the database investigation.
Authors and Affiliations
Bibliographic Information
Book Title: A Practical Hands-on Approach to Database Forensics
Authors: Nhien-An Le-Khac, Kim-Kwang Raymond Choo
Series Title: Studies in Big Data
DOI: https://doi.org/10.1007/978-3-031-16127-8
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
Hardcover ISBN: 978-3-031-16126-1Published: 23 October 2022
Softcover ISBN: 978-3-031-16129-2Published: 24 October 2023
eBook ISBN: 978-3-031-16127-8Published: 21 October 2022
Series ISSN: 2197-6503
Series E-ISSN: 2197-6511
Edition Number: 1
Number of Pages: XIV, 291
Number of Illustrations: 26 b/w illustrations, 192 illustrations in colour
Topics: Data Engineering, Computational Intelligence, Big Data