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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Technical requirements

To get the most out of this chapter, there are a few technical prerequisites that will help you grasp the concepts discussed both in theory and in practice. Here’s a list of what you should be familiar with:

Basic statistics: A good grasp of basic statistical measures such as mean, median, mode, standard deviation, and variance is essential.
Probability: Understanding probability theory, including conditional probabilities and probability distributions, will help you comprehend how these concepts apply to model uncertainty in AI.
Programming language: Knowledge of a programming language, particularly Python, is beneficial since it is widely used in data science and AI. Python libraries such as NumPy, pandas, and SciPy are tools that can help manipulate data and perform statistical analysis.
Data handling: The ability to preprocess and handle data using programming tools will be crucial, especially when working with real-world cybersecurity...