Название: Open Source Intelligence (OSINT) – A practical Introduction: A Field Manual
Автор: Varin Khera, Anand R. Prasad, Suksit Kwanoran
Издательство: River Publishers
Год: 2024
Страниц: 128
Язык: английский
Формат: pdf (true), epub
Размер: 34.5 MB

This practical book introduces open-source intelligence (OSINT) and explores how it can be executed in different intelligence scenarios. It covers varying supporting topics, such as online tracking techniques, privacy best practices for OSINT researchers, and practical examples of OSINT investigations. The book also delves into the integration of Artificial Intelligence (AI) and Machine Learning (ML) in OSINT, social media intelligence methodologies, and the unique characteristics of the surface web, deep web, and dark web. Open-source intelligence (OSINT) is a powerful tool that leverages publicly available data for security purposes. OSINT derives its value from various sources, including the internet, traditional media, academic publications, corporate papers, and geospatial information. Further topics include an examination of the dark web's uses and potential risks, an introduction to digital forensics and its methods for recovering and analyzing digital evidence, and the crucial role of OSINT in digital forensics investigations. The book concludes by addressing the legal considerations surrounding the use of the information and techniques presented. This book provides a comprehensive understanding of CTI, TI, and OSINT. It sets the stage for the best ways to leverage OSINT to support different intelligence needs to support decision-makers in today's complex IT threat landscape. This practical book introduces open-source intelligence (OSINT) and explores how it can be executed in different intelligence scenarios. It covers varying supporting topics, such as online tracking techniques, privacy best practices for OSINT researchers, and practical examples of OSINT investigations.

The first task of an OSINT gathering activity is data collection. OSINT researchers may need to collect considerable volume of data about their targets. Collecting data from all these sources would be daunting for OSINT researchers. Traditional web scraping tools require pre-defined selectors to be set up first that cannot be adjusted after the web scraping activity begins. By leveraging AI tools, they can automate the web scraping process to a great extent, in addition to customizing their gathering activities according to each search case. For instance, AI algorithms are capable of adjusting their work during scraping to gather data from dynamic websites without human intervention.