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Advances in Digital Forensics XV

Overview of attention for book
Cover of 'Advances in Digital Forensics XV'

Table of Contents

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    Book Overview
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    Chapter 1 A Holistic Forensic Model for the Internet of Things
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    Chapter 2 Implementing the Harmonized Model for Digital Evidence Admissibility Assessment
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    Chapter 3 Classifying the Authenticity of Evaluated Smartphone Data
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    Chapter 4 Retrofitting Mobile Devices for Capturing Memory-Resident Malware Based on System Side-Effects
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    Chapter 5 A Targeted Data Extraction System for Mobile Devices
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    Chapter 6 Exploiting Vendor-Defined Messages in the USB Power Delivery Protocol
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    Chapter 7 Detecting Anomalies in Programmable Logic Controllers Using Unsupervised Machine Learning
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    Chapter 8 Creating a Map of User Data in NTFS to Improve File Carving
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    Chapter 9 Analyzing Windows Subsystem for Linux Metadata to Detect Timestamp Forgery
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    Chapter 10 Quick Response Encoding of Human Facial Images for Identity Fraud Detection
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    Chapter 11 Using Neural Networks for Fake Colorized Image Detection
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    Chapter 12 Digital Forensic Atomic Force Microscopy of Semiconductor Memory Arrays
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    Chapter 13 Timeline Visualization of Keywords
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    Chapter 14 DETERMINING THE FORENSIC DATA REQUIREMENTS FOR INVESTIGATING HYPERVISOR ATTACKS
Attention for Chapter 5: A Targeted Data Extraction System for Mobile Devices
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Citations

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Chapter title
A Targeted Data Extraction System for Mobile Devices
Chapter number 5
Book title
Advances in Digital Forensics XV
Published by
Springer, Cham, January 2019
DOI 10.1007/978-3-030-28752-8_5
Book ISBNs
978-3-03-028751-1, 978-3-03-028752-8
Authors

Sudhir Aggarwal, Gokila Dorai, Umit Karabiyik, Tathagata Mukherjee, Nicholas Guerra, Manuel Hernandez, James Parsons, Khushboo Rathi, Hongmei Chi, Temilola Aderibigbe, Rodney Wilson

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 19 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 3 16%
Professor 2 11%
Student > Ph. D. Student 2 11%
Student > Doctoral Student 1 5%
Unknown 11 58%
Readers by discipline Count As %
Computer Science 8 42%
Unknown 11 58%

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