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Apply Open Source Intelligence (OSINT) techniques, methods, and tools to acquire information from publicly available online sources to support your intelligence analysis. Use the harvested data in different scenarios such as financial, crime, and terrorism investigations as well as performing business competition analysis and acquiring intelligence about individuals and other entities. This book will also improve your skills to acquire information online from both the regular Internet as well as the hidden web through its two sub-layers: the deep web and the dark web. The author includes many OSINT resources that can be used by intelligence agencies as well as by enterprises to monitor trends on a global level, identify risks, and gather competitor intelligence so more effective decisions can be made.

Epidemic surveillance using traditional approaches is dependent on case ascertainment and is delayed. Open-source intelligence (OSINT)-based syndromic surveillance can overcome limitations of delayed surveillance and poor case ascertainment, providing early warnings to guide outbreak response. It can identify outbreaks of unknown cause for which no other global surveillance exists. Using the artificial intelligence-based OSINT early warning system EPIWATCH, we describe the global epidemiology of 310 outbreaks of unknown cause that occurred December 31, 2019-January 1, 2023. The outbreaks were associated with 75,968 reported human cases and 4,235 deaths. We identified where OSINT signaled outbreaks earlier than official sources and before diagnoses were made. We identified possible signals of known disease outbreaks with poor case ascertainment. A cause was subsequently reported for only 14% of outbreaks analyzed; the percentage was substantially lower in lower/upper-middle-income economies than high-income economies, highlighting the utility of OSINT-based syndromic surveillance for early warnings, particularly in resource-poor settings.

Algorithms for Automating Open Source Intelligence (OSINT) presents information on the gathering of information and extraction of actionable intelligence from openly available sources, including news broadcasts, public repositories, and more recently, social media.

Deploy deep learning applications into production across multiple platforms. You will work on computer vision applications that use the convolutional neural network (CNN) deep learning model and Python. This book starts by explaining the traditional machine-learning pipeline, where you will analyze an image dataset. Along the way you will cover artificial neural networks (ANNs), building one from scratch in Python, before optimizing it using genetic algorithms. For automating the process, the book highlights the limitations of traditional hand-crafted features for computer vision and why the CNN deep-learning model is the state-of-art solution. CNNs are discussed from scratch to demonstrate how they are different and more efficient than fully connected networks. You will implement a CNN in Python to give you a full understanding of the model. After consolidating the basics, you will use TensorFlow to build a practical image-recognition application and make the pre-trained models accessible over the Internet using Flask. Using Kivy and NumPy, you will create cross-platform data science applications with low overheads. This book will help you apply deep learning and computer vision concepts from scratch, step-by-step from conception to production. You will: Understand how ANNs and CNNs work Create computer vision applications and CNNs from scratch using Python Follow a deep learning project from conception to production using TensorFlow Use NumPy with Kivy to build cross-platform data science applications.

Open source intelligence (OSINT) and web reconnaissance are rich topics for infosec professionals looking for the best ways to sift through the abundance of information widely available online. In many cases, the first stage of any security assessment-that is, reconnaissance-is not given enough attention by security professionals, hackers, and penetration testers. Often, the information openly present is as critical as the confidential data. Hacking Web Intelligence shows you how to dig into the Web and uncover the information many don't even know exists. The book takes a holistic approach that is not only about using tools to find information online but also how to link all the information and transform it into presentable and actionable intelligence. You will also learn how to secure your information online to prevent it being discovered by these reconnaissance methods. Hacking Web Intelligence is an in-depth technical reference covering the methods and techniques you need to unearth open source information from the Internet and utilize it for the purpose of targeted attack during a security assessment. This book will introduce you to many new and leading-edge reconnaissance, information gathering, and open source intelligence methods and techniques, including metadata extraction tools, advanced search engines, advanced browsers, power searching methods, online anonymity tools such as TOR and i2p, OSINT tools such as Maltego, Shodan, Creepy, SearchDiggity, Recon-ng, Social Network Analysis (SNA), Darkweb/Deepweb, data visualization, and much more. Provides a holistic approach to OSINT and Web recon, showing you how to fit all the data together into actionable intelligenceFocuses on hands-on tools such as TOR, i2p, Maltego, Shodan, Creepy, SearchDiggity, Recon-ng, FOCA, EXIF, Metagoofil, MAT, and many moreCovers key technical topics such as metadata searching, advanced browsers and power searching, online anonymity, Darkweb / Deepweb, Social Network Analysis (SNA), and how to manage, analyze, and visualize the data you gatherIncludes hands-on technical examples and case studies, as well as a Python chapter that shows you how to create your own information-gathering tools and modify existing APIs

What is OSINT and what can it do for you? The Internet has become the defining medium for information exchange in the modern world, and the unprecedented success of new web publishing platforms such as those associated with social media has confirmed its dominance as the main information exchange platform for the foreseeable future. But how do you conduct an online investigation when so much of the Internet isn't even indexed by search engines? Accessing and using the information that's freely available online is about more than just relying on the first page of Google results. Open source intelligence (OSINT) is intelligence gathered from publically available sources, and is the key to unlocking this domain for the purposes of investigation. Product overview The Tao of Open Source Cyber Intelligenceprovides a comprehensive guide to OSINT techniques, for the investigator: It catalogues and explains the tools and investigative approaches that are required when conducting research within the surface, deep and dark webs.It explains how to scrutinise criminal activity without compromising your anonymity - and your investigation.It examines the relevance of cyber geography and how to get round its limitationsIt describes useful add-ons for common search engines, as well as considering Metasearch engines (including Dogpile, Zuula, PolyMeta, iSeek, Cluuz, and Carrot2) that collate search data from single-source intelligence platforms such as Google.It considers deep web social media platforms and platform-specific search tools, detailing such concepts as concept mapping, Entity Extraction tools, and specialist search syntax (Google Kung-Fu).It gives comprehensive guidance on Internet security for the smart investigator, and how to strike a balance between security, ease of use and functionality, giving tips on counterintelligence, safe practices, and debunking myths about online privacy. OSINT is a rapidly evolving approach to intelligence collection, and its wide application makes it a useful methodology for numerous practices, including within the criminal investigative community. The Tao of Open Source Cyber Intelligenceis your guide to the cutting edge of this information collection capability. About the author Stewart K. Bertram is a career intelligence analyst who has spent over a decade working across the fields of counterterrorism, cyber security, corporate investigations and geopolitical analysis. The holder of a Master's degree in Computing and a Master of Letters in Terrorism studies, Stewart is uniquely placed at the cutting edge of intelligence and investigation, where technology and established tradecraft combine. Stewart fuses his academic knowledge with significant professional experience, having used open source intelligence on such diverse real-world topics as the terrorist use of social media in Sub-Saharan Africa and threat assessment at the London Olympic Games. Stewart teaches courses on open source intelligence as well as practising what he preaches in his role as a cyber threat intelligence manager for some of the world's leading private-sector intelligence and security agencies.

Explores the politics of open source software, and how it is forcing us to re-think the idea of intellectual property.

The volume of data generated by today’s digitally connected world is enormous, and a significant portion of it is publicly available. These data sources are web archives, public databases, and social networks such as Facebook, Twitter, LinkedIn, Emails, Telegrams, etc. Open-source intelligence (OSINT) extracts information from a collection of publicly available and accessible data. OSINT can provide a solution to the challenges in extracting and gathering intelligence from various publicly available information and social networks. OSINT is currently expanding at an incredible rate, bringing new artificial intelligence-based approaches to address issues of national security, political campaign, the cyber industry, criminal profiling, and society, as well as cyber threats and crimes. In this paper, we have described the current state of OSINT tools/techniques and the state of the art for various applications of OSINT in cyber security. In addition, we have discussed the challenges and future directions to develop autonomous models. These models can provide solutions for different social network-based security, digital forensics, and cyber crime-based problems using various machine learning (ML), deep learning (DL) and artificial intelligence (AI) with OSINT.

Today, open source intelligence (OSINT), i.e., information derived from publicly available sources, makes up between 80 and 90 percent of all intelligence activities carried out by Law Enforcement Agencies (LEAs) and intelligence services in the West. Developments in data mining, machine learning, visual forensics and, most importantly, the growing computing power available for commercial use, have enabled OSINT practitioners to speed up, and sometimes even automate, intelligence collection and analysis, obtaining more accurate results more quickly. As the infosphere expands to accommodate ever-increasing online presence, so does the pool of actionable OSINT. These developments raise important concerns in terms of governance, ethical, legal, and social implications (GELSI). New and crucial oversight concerns emerge alongside standard privacy concerns, as some of the more advanced data analysis tools require little to no supervision. This article offers a systematic review of the relevant literature. It analyzes 571 publications to assess the current state of the literature on the use of AI-powered OSINT (and the development of OSINT software) as it relates to the GELSI framework, highlighting potential gaps and suggesting new research directions.