Machine Learning for Authorship Attribution and Cyber Forensics
Document Type
Book
Source of Publication
International Series on Computer Entertainment and Media Technology
Publication Date
1-1-2020
Abstract
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 potential suspects 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.
DOI Link
ISSN
Publisher
Springer Nature
Disciplines
Computer Sciences
Recommended Citation
Iqbal, Farkhund; Debbabi, Mourad; and Fung, Benjamin C. M., "Machine Learning for Authorship Attribution and Cyber Forensics" (2020). All Works. 2294.
https://zuscholars.zu.ac.ae/works/2294
Indexed in Scopus
no
Open Access
no