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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.
A Research Agenda for Organised Crime
This insightful Research Agenda explores the varied manifestations of organised crime, both on the street and through transnational enterprises, and reveals its impact on the integrity of the financial system. Leading academics identify measures which would disrupt and discourage these threats, however sophisticated, and consider avenues for future research.
The Dark Web
Discussions of the dark web often have sinister connotations, as its capacity to enable various crimes is the aspect that most people fixate upon. However, there is nothing fundamentally criminal about the dark web. It is simply an encrypted part of the internet that allows users to remain anonymous. Nonetheless, a considerable amount of illegal activity does occur on it, making the questions of how it can be monitored and the extent to which it should be pressing issues. This volume explores the various issues related to the dark web, giving readers a better understanding of this enigmatic topic.
Artificial Intelligence and the Future of Defense
Artificial intelligence (AI) is on everybody’s minds these days. Most of the world’s leading companies are making massive investments in it. Governments are scrambling to catch up. Every single one of us who uses Google Search or any of the new digital assistants on our smartphones has witnessed first-hand how quickly these developments now go. Many analysts foresee truly disruptive changes in education, employment, health, knowledge generation, mobility, etc. But what will AI mean for defense and security? In a new study HCSS offers a unique perspective on this question. Most studies to date quickly jump from AI to autonomous (mostly weapon) systems. They anticipate future armed forces ...
AI in Cybersecurity
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.
Data Science in Cybersecurity and Cyberthreat Intelligence
This book presents a collection of state-of-the-art approaches to utilizing machine learning, formal knowledge bases and rule sets, and semantic reasoning to detect attacks on communication networks, including IoT infrastructures, to automate malicious code detection, to efficiently predict cyberattacks in enterprises, to identify malicious URLs and DGA-generated domain names, and to improve the security of mHealth wearables. This book details how analyzing the likelihood of vulnerability exploitation using machine learning classifiers can offer an alternative to traditional penetration testing solutions. In addition, the book describes a range of techniques that support data aggregation and data fusion to automate data-driven analytics in cyberthreat intelligence, allowing complex and previously unknown cyberthreats to be identified and classified, and countermeasures to be incorporated in novel incident response and intrusion detection mechanisms.
Computer Communication, Networking and IoT
This book features a collection of high-quality, peer-reviewed papers presented at the Fifth International Conference on Intelligent Computing and Communication (ICICC 2021) organized by the Department of Computer Science and Engineering and Department of Computer Science and Technology, Dayananda Sagar University, Bengaluru, India, on November 26 – 27, 2021. The book is organized in two volumes and discusses advanced and multi-disciplinary research regarding the design of smart computing and informatics. It focuses on innovation paradigms in system knowledge, intelligence, and sustainability that can be applied to provide practical solutions to a number of problems in society, the environment and industry. Further, the book also addresses the deployment of emerging computational and knowledge transfer approaches, optimizing solutions in various disciplines of science, technology, and healthcare.
Machine Learning Approaches in Cyber Security Analytics
This book introduces various machine learning methods for cyber security analytics. With an overwhelming amount of data being generated and transferred over various networks, monitoring everything that is exchanged and identifying potential cyber threats and attacks poses a serious challenge for cyber experts. Further, as cyber attacks become more frequent and sophisticated, there is a requirement for machines to predict, detect, and identify them more rapidly. Machine learning offers various tools and techniques to automate and quickly predict, detect, and identify cyber attacks.
