Machine Learning For Authorship Attribution And Cyber Forensics
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Machine Learning for Authorship Attribution and Cyber Forensics
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.
Cyber Security Cryptography and Machine Learning
This book constitutes the proceedings of the first International Symposium on Cyber Security Cryptography and Machine Learning, held in Beer-Sheva, Israel, in June 2017. The 17 full and 4 short papers presented include cyber security; secure software development methodologies, formal methods semantics and verification of secure systems; fault tolerance, reliability, availability of distributed secure systems; game-theoretic approaches to secure computing; automatic recovery of self-stabilizing and self-organizing systems; communication, authentication and identification security; cyber security for mobile and Internet of things; cyber security of corporations; security and privacy for cloud, edge and fog computing; cryptography; cryptographic implementation analysis and construction; secure multi-party computation; privacy-enhancing technologies and anonymity; post-quantum cryptography and security; machine learning and big data; anomaly detection and malware identification; business intelligence and security; digital forensics; digital rights management; trust management and reputation systems; information retrieval, risk analysis, DoS.
AI-Driven: Social Media Analytics and Cybersecurity
This book presents state-of-the-art research, conceptual frameworks, and practical solutions, focusing on the intersection of these vital fields. The ever-evolving digital landscape has fostered a close relationship between social media and cybersecurity. Both social media analytics and cybersecurity are prominent research areas that shape the lives of individuals, organizations, and communities. It covers three key categories: First, social media analytics, which explores how data from platforms like Twitter and Facebook is harnessed for insights, sentiment analysis, and trend predictions. Second, cybersecurity and digital safety, which addresses emerging threats and explores tools and strategies to secure digital spaces. Third, advanced technologies and their broader impacts, which examines the technologies shaping social media platforms. This book is an invaluable resource for researchers, professionals, and students, providing comprehensive insights into the application of advanced technologies and analytical techniques for safeguarding digital environments. It is essential reading for anyone interested in social media analytics, digital safety, and the future of technology.