Big Digital Forensic Data

Volume 1: Data Reduction Framework and Selective Imaging

  • Darren Quick
  • Kim-Kwang Raymond Choo

Part of the SpringerBriefs on Cyber Security Systems and Networks book series (BRIEFSCSSN)

Table of contents

  1. Front Matter
    Pages i-xv
  2. Darren Quick, Kim-Kwang Raymond Choo
    Pages 1-4
  3. Darren Quick, Kim-Kwang Raymond Choo
    Pages 5-45
  4. Darren Quick, Kim-Kwang Raymond Choo
    Pages 47-67
  5. Darren Quick, Kim-Kwang Raymond Choo
    Pages 69-92
  6. Darren Quick, Kim-Kwang Raymond Choo
    Pages 93-96

About this book

Introduction

This book provides an in-depth understanding of big data challenges to digital forensic investigations, also known as big digital forensic data. It also develops the basis of using data mining in big forensic data analysis, including data reduction, knowledge management, intelligence, and data mining principles to achieve faster analysis in digital forensic investigations. By collecting and assembling a corpus of test data from a range of devices in the real world, it outlines a process of big data reduction, and evidence and intelligence extraction methods. Further, it includes the experimental results on vast volumes of real digital forensic data. The book is a valuable resource for digital forensic practitioners, researchers in big data, cyber threat hunting and intelligence, data mining and other related areas.

Keywords

Big forensic data Big digital forensic data Cyber forensics Cyber threat evidence Cyber threat hunting Cyber threat intelligence Digital forensics

Authors and affiliations

  • Darren Quick
    • 1
  • Kim-Kwang Raymond Choo
    • 2
  1. 1.University of South AustraliaAdelaideAustralia
  2. 2.University of Texas at San AntonioSan AntonioUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-981-10-7763-0
  • Copyright Information The Author(s) 2018
  • Publisher Name Springer, Singapore
  • eBook Packages Computer Science
  • Print ISBN 978-981-10-7762-3
  • Online ISBN 978-981-10-7763-0
  • Series Print ISSN 2522-5561
  • Series Online ISSN 2522-557X
  • About this book