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2018 | Book

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Big Digital Forensic Data

Volume 1: Data Reduction Framework and Selective Imaging

Authors: Darren Quick, Kim-Kwang Raymond Choo

Publisher: Springer Singapore

Book Series : SpringerBriefs on Cyber Security Systems and Networks

Part of: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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About this book

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.

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Table of Contents

Frontmatter

Chapter 1. Introduction

Abstract

Digital forensic analysis is the process of identification, preservation, analysis, and presentation of digital evidence in a manner that is legally acceptable (McKemmish 1999). The significant growth in the size of storage media combined with the popularity of digital devices and the decrease in the price of these devices and storage media have led to a major issue affecting the timely process of justice, which is the growing volume of data seized and presented for analysis, often now consisting of many terabytes of data for each investigation.

Darren Quick, Kim-Kwang Raymond Choo

Chapter 2. Background and Literature Review

Abstract

Big Data has been defined as “high-volume, high-velocity and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making” (Gartner in IT glossary: big data, 2013).

Darren Quick, Kim-Kwang Raymond Choo

Chapter 3. Data Reduction and Data Mining Frame-Work

Abstract

As highlighted in Chap. 2, there is a need for a methodology and framework for data reduction and data mining of digital forensic data. This chapter outlines the digital forensic data reduction and data mining framework, which endeavours to expand the process used for traditional forensic computer analysis to include data reduction, data mining, and input from external source data. This serves to expand common digital forensic frameworks, to be applicable when dealing with a large volume of digital forensic data.

Darren Quick, Kim-Kwang Raymond Choo

Chapter 4. Digital Forensic Data Reduction by Selective Imaging

Abstract

In the previous chapters, the focus of the research was outlined, current literature was discussed, and the proposed Digital Forensic Data Reduction Framework was explained. This chapter focuses on Step 5 of the framework and explores the process of data reduction using the proposed framework to guide the research.

Darren Quick, Kim-Kwang Raymond Choo

Chapter 5. Summary of the Framework and DRbSI

Abstract

The main theme of this research is an examination of the data volume issue affecting digital forensic analysis demands, and to research and propose valid methods to address the increasing volume of devices and data with methodologies encompassed in a framework which is applicable to real world investigation demands.

Darren Quick, Kim-Kwang Raymond Choo

Title: Big Digital Forensic Data
Authors: Darren Quick
Kim-Kwang Raymond Choo
Copyright Year: 2018
Publisher: Springer Singapore
Electronic ISBN: 978-981-10-7763-0
Print ISBN: 978-981-10-7762-3
DOI: https://doi.org/10.1007/978-981-10-7763-0

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