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Advances in Digital Forensics XIII

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

Table of Contents

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    Book Overview
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    Chapter 1 Establishing Findings in Digital Forensic Examinations: A Case Study Method
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    Chapter 2 A Model for Digital Evidence Admissibility Assessment
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    Chapter 3 Evaluating the Authenticity of Smartphone Evidence
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    Chapter 4 Forensic Evaluation of an Amazon Fire TV Stick
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    Chapter 5 Detecting Anomalous Programmable Logic Controller Events Using Machine Learning
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    Chapter 6 A Forensic Methodology for Software-Defined Network Switches
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    Chapter 7 Identifying Evidence for Cloud Forensic Analysis
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    Chapter 8 Digital Forensic Implications of Collusion Attacks on the Lightning Network
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    Chapter 9 Insider Threat Detection Using Time-Series-Based Raw Disk Forensic Analysis
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    Chapter 10 Anti-Forensic Threat Modeling
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    Chapter 11 A Behavior-Based Approach for Malware Detection
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    Chapter 12 Categorizing Mobile Device Malware Based on System Side-Effects
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    Chapter 13 Semantic Video Carving Using Perceptual Hashing and Optical Flow
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    Chapter 14 Detecting Fraudulent Bank Checks
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    Chapter 15 Automated Collection and Correlation of File Provenance Information
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    Chapter 16 Using Personal Information in Targeted Grammar-Based Probabilistic Password Attacks
Attention for Chapter 5: Detecting Anomalous Programmable Logic Controller Events Using Machine Learning
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Citations

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Chapter title
Detecting Anomalous Programmable Logic Controller Events Using Machine Learning
Chapter number 5
Book title
Advances in Digital Forensics XIII
Published by
Springer, Cham, January 2017
DOI 10.1007/978-3-319-67208-3_5
Book ISBNs
978-3-31-967207-6, 978-3-31-967208-3
Authors

Ken Yau, Kam-Pui Chow

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 9 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 22%
Student > Bachelor 1 11%
Researcher 1 11%
Unknown 5 56%
Readers by discipline Count As %
Computer Science 2 22%
Engineering 2 22%
Unknown 5 56%

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