Gianni D'Angelo – författare

Cyber Threat Intelligence for Cloud Systems: A Comprehensive Guide to Becoming an Analyst begins with the foundational study of asset vulnerabilities, laying the groundwork for understanding the weak points within cloud environments. It then introduces tools and methodologies for the automated collection and supervision of data from assets, culminating in the exploration of advanced techniques, such as Artificial Intelligence and Machine Learning for data analysis and automated threat response. Alongside theoretical concepts, the book includes practical examples, hands-on exercises in Python, existing tools, and real-world case studies to help readers solidify their understanding and apply what they’ve learned.Cybersecurity is one of the fastest-growing fields in today’s technological landscape, and with the increasing complexity and pervasiveness of cyber threats, especially in cloud environments, the need for qualified professionals such as Cyber Threat Analysts has never been more critical. Here, author Gianni D’Angelo provides the technical knowledge required to analyze and defend digital infrastructures, covering both traditional and modern approaches, highlighting how CTI supports decision-making and proactive defense strategies.Offers a structured learning path that guides readers from the fundamentals of cybersecurity to the specialized knowledge required for working in Cyber Threat Intelligence (CTI) within cloud environmentsIntroduces the foundational principles of CTI, including threat modeling, vulnerability assessment, data collection and correlation, and the lifecycle of threat intelligencePresents a mix of theoretical frameworks, practical exercises, and real-world scenarios, providing readers with the essential skills and mindset to identify, analyze, and respond to cyber threats effectivelyCovers advanced techniques involving Artificial Intelligence (AI) and Machine Learning (ML) to automate the detection and analysis of cyber threatsDemonstrates how to use tools and frameworks to process large volumes of threat data, recognize patterns, and enable rapid, automated responses to security incidentsIncludes hands-on Python exercises and case studies where readers can learn how to apply AI-driven techniques to real-world cloud environments, thus improving detection accuracy and response speed

Cyber Threat Intelligence for Cloud Systems: A Comprehensive Guide to Becoming an Analyst begins with the foundational study of asset vulnerabilities, laying the groundwork for understanding the weak points within cloud environments. It then introduces tools and methodologies for the automated collection and supervision of data from assets, culminating in the exploration of advanced techniques, such as Artificial Intelligence and Machine Learning for data analysis and automated threat response. Alongside theoretical concepts, the book includes practical examples, hands-on exercises in Python, existing tools, and real-world case studies to help readers solidify their understanding and apply what they've learned.Cybersecurity is one of the fastest-growing fields in today's technological landscape, and with the increasing complexity and pervasiveness of cyber threats, especially in cloud environments, the need for qualified professionals such as Cyber Threat Analysts has never been more critical. Here, author Gianni D'Angelo provides the technical knowledge required to analyze and defend digital infrastructures, covering both traditional and modern approaches, highlighting how CTI supports decision-making and proactive defense strategies. - Offers a structured learning path that guides readers from the fundamentals of cybersecurity to the specialized knowledge required for working in Cyber Threat Intelligence (CTI) within cloud environments- Introduces the foundational principles of CTI, including threat modeling, vulnerability assessment, data collection and correlation, and the lifecycle of threat intelligence- Presents a mix of theoretical frameworks, practical exercises, and real-world scenarios, providing readers with the essential skills and mindset to identify, analyze, and respond to cyber threats effectively- Covers advanced techniques involving Artificial Intelligence (AI) and Machine Learning (ML) to automate the detection and analysis of cyber threats- Demonstrates how to use tools and frameworks to process large volumes of threat data, recognize patterns, and enable rapid, automated responses to security incidents- Includes hands-on Python exercises and case studies where readers can learn how to apply AI-driven techniques to real-world cloud environments, thus improving detection accuracy and response speed

This book presents the major advances in techniques to preserve privacy and security requirements connected with the use of AI and machine learning (ML) to analyse and manage sensitive data in MCPSs. The advances in Internet of things and artificial intelligence (AI) have witnessed great progress on healthcare technologies in several application domains. In particular, the interconnection between the physical spaces, characterized by physical devices able to collect users’ health information, with the cyberspace, also known as the virtual space, has fostered the development of intelligent Medical Cyber-Physical Systems (MCPSs) with the capability to deliver real-time healthcare services. On the other hand, the potential innovation that these technologies bring to improve patient care, by remotely analysing health parameters using medical devices, advanced smart sensors, and AI, is hampered by security and privacy challenges related to the managed sensitive data. Starting from the state of the art on AI and ML for medical applications and digital health, an accurate analysis of privacy and security risks associated with the use of the MCPSs is presented. Then, Digital Twins are introduced as a significant technique to enhance decision-making through learning and reasoning of collected on-field real-time data. Moreover, decentralized healthcare data management approaches based on federated learning, tiny machine learning, and blockchain technologies have been introduced to shift control and responsibility of healthcare data management from individual centralized entities to a more distributed structure, preserving privacy and security. Finally, the application of AI-based security monitoring approaches in healthcare is discussed. In this book, both theoretical and practical approaches are used to allow readers to understand complex topics and concepts easily also through real-life scenarios.