This book contains research contributions from leading cyber security scholars from around the world. The authors provide comprehensive coverage of various cyber security topics, while highlighting recent trends. The book also contains a compendium of definitions and explanations of concepts, processes, acronyms, and comprehensive references on existing literature and research on cyber security and analytics, information sciences, decision systems, digital forensics, and related fields. As a whole, the book is a solid reference for dynamic and innovative research in the field, with a focus on design and development of future-ready cyber security measures. Topics include defenses against ransomware, phishing, malware, botnets, insider threats, and many others.

As you read this your computer is in jeopardy of being hacked and your identity being stolen. How can you protect yourself? The world's foremost cyber security experts from FBI Director Robert S. Mueller, III to Special Assistant to the President Howard A. Schmidt, share critical practical knowledge on how the cyberspace ecosystem is structured, how it functions, and what we can do to protect it and ourselves from attack

This book reports on the latest research and developments in the field of human factors in cybersecurity. It analyzes how the human vulnerabilities can be exploited by cybercriminals and proposes methods and tools to increase cybersecurity awareness. The chapters cover the social, economic and behavioral aspects of the cyberspace, providing a comprehensive perspective to manage cybersecurity risks. By gathering the proceedings of the AHFE Virtual Conference on Human Factors Cybersecurity, held on July 16–20, 2020, this book offers a timely perspective of key psychological and organizational factors influencing cybersecurity, reporting on technical tools, training methods and personnel management strategies that should enable achieving a holistic cyber protection for both individuals and organizations. By combining concepts and methods of engineering, education, computer science and psychology, it offers an inspiring guide for researchers and professionals, as well as decision-makers, working at the interfaces of those fields.

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.

Information Security Analytics gives you insights into the practice of analytics and, more importantly, how you can utilize analytic techniques to identify trends and outliers that may not be possible to identify using traditional security analysis techniques. Information Security Analytics dispels the myth that analytics within the information security domain is limited to just security incident and event management systems and basic network analysis. Analytic techniques can help you mine data and identify patterns and relationships in any form of security data. Using the techniques covered in this book, you will be able to gain security insights into unstructured big data of any type. The authors of Information Security Analytics bring a wealth of analytics experience to demonstrate practical, hands-on techniques through case studies and using freely-available tools that will allow you to find anomalies and outliers by combining disparate data sets. They also teach you everything you need to know about threat simulation techniques and how to use analytics as a powerful decision-making tool to assess security control and process requirements within your organization. Ultimately, you will learn how to use these simulation techniques to help predict and profile potential risks to your organization. - Written by security practitioners, for security practitioners - Real-world case studies and scenarios are provided for each analytics technique - Learn about open-source analytics and statistical packages, tools, and applications - Step-by-step guidance on how to use analytics tools and how they map to the techniques and scenarios provided - Learn how to design and utilize simulations for "what-if" scenarios to simulate security events and processes - Learn how to utilize big data techniques to assist in incident response and intrusion analysis

Emerging Trends in ICT Security, an edited volume, discusses the foundations and theoretical aspects of ICT security; covers trends, analytics, assessments and frameworks necessary for performance analysis and evaluation; and gives you the state-of-the-art knowledge needed for successful deployment of security solutions in many environments. Application scenarios provide you with an insider's look at security solutions deployed in real-life scenarios, including but limited to smart devices, biometrics, social media, big data security, and crowd sourcing. - Provides a multidisciplinary approach to security with coverage of communication systems, information mining, policy making, and management infrastructures - Discusses deployment of numerous security solutions, including, cyber defense techniques and defense against malicious code and mobile attacks - Addresses application of security solutions in real-life scenarios in several environments, such as social media, big data and crowd sourcing

Cybersecurity Analytics is for the cybersecurity student and professional who wants to learn data science techniques critical for tackling cybersecurity challenges, and for the data science student and professional who wants to learn about cybersecurity adaptations. Trying to build a malware detector, a phishing email detector, or just interested in finding patterns in your datasets? This book can let you do it on your own. Numerous examples and datasets links are included so that the reader can "learn by doing." Anyone with a basic college-level calculus course and some probability knowledge can easily understand most of the material. The book includes chapters containing: unsupervised learning, semi-supervised learning, supervised learning, text mining, natural language processing, and more. It also includes background on security, statistics, and linear algebra. The website for the book contains a listing of datasets, updates, and other resources for serious practitioners.