You're reading from Artificial Intelligence for Cybersecurity Develop AI approaches to solve cybersecurity problems in your organization

Product type Paperback

Published in Oct 2024

Publisher Packt

ISBN-13 9781805124962

Length 358 pages

Edition 1st Edition

Concepts

Data Governance

Authors (4):

Bojan Kolosnjaji

Apostolis Zarras

Huang Xiao

Peng Xu

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Table of Contents (27) Chapters

Preface

1. Part 1: Data-Driven Cybersecurity and AI FREE CHAPTER

2. Chapter 1: Big Data in Cybersecurity

3. Chapter 2: Automation in Cybersecurity

4. Chapter 3: Cybersecurity Data Analytics

5. Part 2: AI and Where It Fits In

6. Chapter 4: AI, Machine Learning, and Statistics - A Taxonomy

7. Chapter 5: AI Problems and Methods

8. Chapter 6: Workflow, Tools, and Libraries in AI Projects

9. Part 3: Applications of AI in Cybersecurity

10. Chapter 7: Malware and Network Intrusion Detection and Analysis

11. Chapter 8: User and Entity Behavior Analysis

12. Chapter 9: Fraud, Spam, and Phishing Detection

13. Chapter 10: User Authentication and Access Control

14. Chapter 11: Threat Intelligence

15. Chapter 12: Anomaly Detection in Industrial Control Systems

16. Chapter 13: Large Language Models and Cybersecurity

17. Part 4: Common Problems When Applying AI in Cybersecurity

18. Chapter 14: Data Quality and its Usage in the AI and LLM Era

19. Chapter 15: Correlation, Causation, Bias, and Variance

20. Chapter 16: Evaluation, Monitoring, and Feedback Loop

21. Chapter 17: Learning in a Changing and Adversarial Environment

22. Chapter 18: Privacy, Accountability, Explainability, and Trust – Responsible AI

23. Part 5: Final Remarks and Takeaways

24. Chapter 19: Summary

25. Index

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26. Other Books You May Enjoy

Workflow of AI projects

Starting in November 2022, the workflow of AI projects shifted and extended to a more extensive scope because of OpenAI’s ChatGPT [1]. After OpenAI released ChatGPT, AI-powered projects attracted technical persons and people without technical backgrounds. That means the workflow of AI projects not only covers its technical content but also extends to how to integrate them into products. Traditionally, when we discuss the workflow of AI projects, we mainly define this process as combining data preparation, AI modeling, simulation and evaluation, and deployment. That is most often defined as workflow in AI projects. Meanwhile, currently, especially with the development of large language (pre-trained) models (LLMs) and how to integrate them into AI products, the definition of “workflow” in AI projects shifts to AI-powered projects, which also includes how to incorporate the AI projects into a production environment.

In this section, we...

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Table of Contents (27) Chapters

Workflow of AI projects

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Authors (4)

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