Authors:
- Unified approach to investigate digital crimes and identify suspects together with their collaborators and facilitators
- Customized data mining and machine learning methods for investigating cyber-attacks and online crimes
- In-depth study of methods in evidenced-based authorship analysis
Part of the book series: International Series on Computer, Entertainment and Media Technology (ISCEMT)
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Table of contents (11 chapters)
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Front Matter
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Back Matter
About this book
The book first explores the cybersecurity’s landscape and the inherent susceptibility of online communication system such as e-mail, chat conversation and social media in cybercrimes. Common sources and resources of digital crimes, their causes and effects together with the emerging threats for society are illustrated in this book. This book not only explores the growing needs of cybersecurity and digital forensics but also investigates relevant technologies and methods to meet the said needs. Knowledge discovery, machine learning and data analytics are explored for collecting cyber-intelligence and forensics evidence on cybercrimes.
Online communication documents, which are the main source of cybercrimes are investigated from two perspectives: the crime and the criminal. AI and machine learning methods are applied to detect illegal and criminal activities such as bot distribution, drug trafficking and child pornography. Authorship analysis is applied to identify the potentialsuspects and their social linguistics characteristics. Deep learning together with frequent pattern mining and link mining techniques are applied to trace the potential collaborators of the identified criminals.
Finally, the aim of the book is not only to investigate the crimes and identify the potential suspects but, as well, to collect solid and precise forensics evidence to prosecute the suspects in the court of law.
Keywords
- Cybercrime
- Forensic investigation
- Cyber forensics
- Crime investigation
- Data mining
- Classification
- Clustering
- Authorship analysis
- Statistical analysis
- Authorship identification
- Authorship characterization
- Rule mining
- Cybercrimes
- Criminal networks
- Writeprint
- Stylometric features
- Anonymity
- Associative classification
Authors and Affiliations
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Zayed University, Abu Dhabi, United Arab Emirates
Farkhund Iqbal
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School of Engineering & Computer Science, Concordia University, Montreal, Canada
Mourad Debbabi
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School of Information Studies, McGill University, Montreal, Canada
Benjamin C. M. Fung
Bibliographic Information
Book Title: Machine Learning for Authorship Attribution and Cyber Forensics
Authors: Farkhund Iqbal, Mourad Debbabi, Benjamin C. M. Fung
Series Title: International Series on Computer, Entertainment and Media Technology
DOI: https://doi.org/10.1007/978-3-030-61675-5
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020
Hardcover ISBN: 978-3-030-61674-8Published: 05 December 2020
Softcover ISBN: 978-3-030-61677-9Published: 05 December 2021
eBook ISBN: 978-3-030-61675-5Published: 04 December 2020
Series ISSN: 2364-947X
Series E-ISSN: 2364-9488
Edition Number: 1
Number of Pages: IX, 158
Number of Illustrations: 10 b/w illustrations, 28 illustrations in colour
Topics: Data Mining and Knowledge Discovery, Machine Learning, Cybercrime