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.
1. Chapter 1: Introduction and motivations - p. 3 2. Chapter 2: Cyber threat intelligence - p. 13 3. Chapter 3: What to protect and from whom: an analysis of threats and actors - p. 31 4. Chapter 4: Cloud architecture - p. 51 5. Chapter 5: Vulnerabilities: fundamentals, taxonomy, characterization, and discovery - p. 67 6. Chapter 6: Cloud vulnerabilities - p. 93 7. Part II: Practical cloud security and defense - p. P2 8. Chapter 7: Frameworks for effective threat monitoring and analysis: MITRE ATT&CK & SIEM - p. 143 9. Chapter 8: Automated threat detection and response - p. 165 10. Chapter 9: Virtual lab for threat analysis: ethics, network architecture, and attack lifecycle - p. 197 11. Chapter 10: Analyzing and simulating attacks on OpenStack infrastructures - p. 223 12. Part III: Cyber data analytics - p. P3 13. Chapter 11: Cybersecurity and Big Data - p. 251 14. Chapter 12: Machine learning and artificial intelligence for cyber big data analysis - p. 271 15. Chapter 13: Practical data mining with WEKA - p. 297 16. Chapter 14: Python libraries for threat intelligence - p. 329 17. Part IV: Understanding network traffic - p. P4 18. Chapter 15: Intrusion detection systems - p. 369 19. Chapter 16: Network traffic analysis and packet processing - p. 387 20. Chapter 17: Network traffic feature extraction and analysis - p. 407 21. Chapter 18: Network traffic classification - p. 439 22. Chapter 19: Concluding remarks and future directions - p. 461 23. Appendix A: Linux essentials: installation, commands, and tools - p. 465 24. Appendix B: Networking essentials: protocols and architecture - p. 481 25. Appendix C: The basics of neural networks - p. 495 26. Bibliography - p. 507 27. Index - p. 517
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.
1. Chapter 1: Introduction and motivations - p. 3 2. Chapter 2: Cyber threat intelligence - p. 13 3. Chapter 3: What to protect and from whom: an analysis of threats and actors - p. 31 4. Chapter 4: Cloud architecture - p. 51 5. Chapter 5: Vulnerabilities: fundamentals, taxonomy, characterization, and discovery - p. 67 6. Chapter 6: Cloud vulnerabilities - p. 93 7. Part II: Practical cloud security and defense - p. P2 8. Chapter 7: Frameworks for effective threat monitoring and analysis: MITRE ATT&CK & SIEM - p. 143 9. Chapter 8: Automated threat detection and response - p. 165 10. Chapter 9: Virtual lab for threat analysis: ethics, network architecture, and attack lifecycle - p. 197 11. Chapter 10: Analyzing and simulating attacks on OpenStack infrastructures - p. 223 12. Part III: Cyber data analytics - p. P3 13. Chapter 11: Cybersecurity and Big Data - p. 251 14. Chapter 12: Machine learning and artificial intelligence for cyber big data analysis - p. 271 15. Chapter 13: Practical data mining with WEKA - p. 297 16. Chapter 14: Python libraries for threat intelligence - p. 329 17. Part IV: Understanding network traffic - p. P4 18. Chapter 15: Intrusion detection systems - p. 369 19. Chapter 16: Network traffic analysis and packet processing - p. 387 20. Chapter 17: Network traffic feature extraction and analysis - p. 407 21. Chapter 18: Network traffic classification - p. 439 22. Chapter 19: Concluding remarks and future directions - p. 461 23. Appendix A: Linux essentials: installation, commands, and tools - p. 465 24. Appendix B: Networking essentials: protocols and architecture - p. 481 25. Appendix C: The basics of neural networks - p. 495 26. Bibliography - p. 507 27. Index - p. 517
