Darkweb Cyber Threat Intelligence Mining

This book describes techniques and results in cyber threat intelligence from the center of the malicious hacking underworld - the dark web.

The University of Arizona Artificial Intelligence Lab (AI Lab) Dark Web project is a long-term scientific research program that aims to study and understand the international terrorism (Jihadist) phenomena via a computational, data-centric approach. We aim to collect "ALL" web content generated by international terrorist groups, including web sites, forums, chat rooms, blogs, social networking sites, videos, virtual world, etc. We have developed various multilingual data mining, text mining, and web mining techniques to perform link analysis, content analysis, web metrics (technical sophistication) analysis, sentiment analysis, authorship analysis, and video analysis in our research. The approaches and methods developed in this project contribute to advancing the field of Intelligence and Security Informatics (ISI). Such advances will help related stakeholders to perform terrorism research and facilitate international security and peace. This monograph aims to provide an overview of the Dark Web landscape, suggest a systematic, computational approach to understanding the problems, and illustrate with selected techniques, methods, and case studies developed by the University of Arizona AI Lab Dark Web team members. This work aims to provide an interdisciplinary and understandable monograph about Dark Web research along three dimensions: methodological issues in Dark Web research; database and computational techniques to support information collection and data mining; and legal, social, privacy, and data confidentiality challenges and approaches. It will bring useful knowledge to scientists, security professionals, counterterrorism experts, and policy makers. The monograph can also serve as a reference material or textbook in graduate level courses related to information security, information policy, information assurance, information systems, terrorism, and public policy.

This book provides readers with up-to-date research of emerging cyber threats and defensive mechanisms, which are timely and essential. It covers cyber threat intelligence concepts against a range of threat actors and threat tools (i.e. ransomware) in cutting-edge technologies, i.e., Internet of Things (IoT), Cloud computing and mobile devices. This book also provides the technical information on cyber-threat detection methods required for the researcher and digital forensics experts, in order to build intelligent automated systems to fight against advanced cybercrimes. The ever increasing number of cyber-attacks requires the cyber security and forensic specialists to detect, analyze and defend against the cyber threats in almost real-time, and with such a large number of attacks is not possible without deeply perusing the attack features and taking corresponding intelligent defensive actions – this in essence defines cyber threat intelligence notion. However, such intelligence would not be possible without the aid of artificial intelligence, machine learning and advanced data mining techniques to collect, analyze, and interpret cyber-attack campaigns which is covered in this book. This book will focus on cutting-edge research from both academia and industry, with a particular emphasis on providing wider knowledge of the field, novelty of approaches, combination of tools and so forth to perceive reason, learn and act on a wide range of data collected from different cyber security and forensics solutions. This book introduces the notion of cyber threat intelligence and analytics and presents different attempts in utilizing machine learning and data mining techniques to create threat feeds for a range of consumers. Moreover, this book sheds light on existing and emerging trends in the field which could pave the way for future works. The inter-disciplinary nature of this book, makes it suitable for a wide range of audiences with backgrounds in artificial intelligence, cyber security, forensics, big data and data mining, distributed systems and computer networks. This would include industry professionals, advanced-level students and researchers that work within these related fields.

Inside the Dark Web provides a broad overview of emerging digital threats and computer crimes, with an emphasis on cyberstalking, hacktivism, fraud and identity theft, and attacks on critical infrastructure. The book also analyzes the online underground economy and digital currencies and cybercrime on the dark web. The book further explores how dark web crimes are conducted on the surface web in new mediums, such as the Internet of Things (IoT) and peer-to-peer file sharing systems as well as dark web forensics and mitigating techniques. This book starts with the fundamentals of the dark web along with explaining its threat landscape. The book then introduces the Tor browser, which is used to access the dark web ecosystem. The book continues to take a deep dive into cybersecurity criminal activities in the dark net and analyzes the malpractices used to secure your system. Furthermore, the book digs deeper into the forensics of dark web, web content analysis, threat intelligence, IoT, crypto market, and cryptocurrencies. This book is a comprehensive guide for those who want to understand the dark web quickly. After reading Inside the Dark Web, you’ll understand The core concepts of the dark web. The different theoretical and cross-disciplinary approaches of the dark web and its evolution in the context of emerging crime threats. The forms of cybercriminal activity through the dark web and the technological and "social engineering" methods used to undertake such crimes. The behavior and role of offenders and victims in the dark web and analyze and assess the impact of cybercrime and the effectiveness of their mitigating techniques on the various domains. How to mitigate cyberattacks happening through the dark web. The dark web ecosystem with cutting edge areas like IoT, forensics, and threat intelligence and so on. The dark web-related research and applications and up-to-date on the latest technologies and research findings in this area. For all present and aspiring cybersecurity professionals who want to upgrade their skills by understanding the concepts of the dark web, Inside the Dark Web is their one-stop guide to understanding the dark web and building a cybersecurity plan.

Cutting-edge models for proactive cybersecurity, applying AI, learning, and network analysis to information mined from hacker communities.

This book brings together the essential methodologies required to understand the advancement of digital technologies into digital transformation, as well as to protect them against cyber threat vulnerabilities (in this context cybersecurity attack ontology is included, modeling different types of adversary knowledge). It covers such essential methodologies as CIA Triad, Security Risk, Likelihood, and Consequence Level, Threat Attack Profiling, Threat Intelligence, Threat Lifecycle and more. The idea behind digital transformation is to use digital technologies not only to replicate an existing process in a digital form, but to use digital technology to transform that process into something intelligent (where anything is connected with everything at any time and accessible and controlled and designed advanced). Against this background, cyber threat attacks become reality, using advanced digital technologies with their extreme interconnected capability which call for sophisticated cybersecurity protecting digital technologies of digital transformation. Scientists, advanced-level students and researchers working in computer science, electrical engineering and applied mathematics will find this book useful as a reference guide. Professionals working in the field of big data analytics or digital/intelligent manufacturing will also find this book to be a valuable tool.

This book presents a collection of state-of-the-art AI approaches to cybersecurity and cyberthreat intelligence, offering strategic defense mechanisms for malware, addressing cybercrime, and assessing vulnerabilities to yield proactive rather than reactive countermeasures. The current variety and scope of cybersecurity threats far exceed the capabilities of even the most skilled security professionals. In addition, analyzing yesterday’s security incidents no longer enables experts to predict and prevent tomorrow’s attacks, which necessitates approaches that go far beyond identifying known threats. Nevertheless, there are promising avenues: complex behavior matching can isolate threats based on the actions taken, while machine learning can help detect anomalies, prevent malware infections, discover signs of illicit activities, and protect assets from hackers. In turn, knowledge representation enables automated reasoning over network data, helping achieve cybersituational awareness. Bringing together contributions by high-caliber experts, this book suggests new research directions in this critical and rapidly growing field.

In today’s digital transformation environments, a rigorous cybersecurity approach to effective risk management — including contingency planning, outlining immediate actions, preparing post-breach responses — is central to defending organizations’ interconnected computer systems, networks, and infrastructure resources from malicious cyber-attacks. Specifically, cybersecurity technologies, processes, and practices need to be generalized and applied to intrusion detection and prevention measures. This entails analyzing profiles of cyber-attackers and building cyber-attack models for behavior simulation that can effectively counter such attacks. This comprehensive volume aims to cover all essential aspects of cybersecurity in digital transformation and to provide a framework for considering the many objectives and requirements involved. In addition to introducing theoretical foundations, the work also offers practical techniques for defending against malicious cybercriminals. Topics and features: Explores cybersecurity’s impact on the dynamics of interconnected, complex cyber- and physical systems, infrastructure resources, and networks Provides numerous examples of applications and best practices Considers methods that organizations can use to assess their cybersecurity awareness and/or strategy Describes anomaly intrusion detection, a key tool in thwarting both malware and theft (whether by insiders or external parties) of corporate data Addresses cyber-attacker profiles, cyber-attack models and simulation, cybersecurity ontology, access-control mechanisms, and policies for handling ransomware attacks Discusses the NIST Cybersecurity Framework, MITRE Adversarial Tactics, Techniques and Common Knowledge, CIS Critical Security Controls, and the ISA/IEC 62442 Cybersecurity Standard Gathering all the relevant information, this practical guide is eminently suitable as a self-study resource for engineers, scientists, computer scientists, and chief information officers. Further, with its many examples of best practices, it can serve as an excellent text for graduate-level courses and research into cybersecurity. Dietmar P. F. Möller, a retired full professor, is affiliated with the Institute for Mathematics at Clausthal University of Technology, Germany. He was an author of several other Springer titles, including Guide to Automotive Connectivity and Cybersecurity.