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Artificial Intelligence for Cybersecurity

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

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Product type Paperback
Published in Oct 2024
Publisher Packt
ISBN-13 9781805124962
Length 358 pages
Edition 1st Edition
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Authors (4):
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Bojan Kolosnjaji Bojan Kolosnjaji
Author Profile Icon Bojan Kolosnjaji
Bojan Kolosnjaji
Apostolis Zarras Apostolis Zarras
Author Profile Icon Apostolis Zarras
Apostolis Zarras
Huang Xiao Huang Xiao
Author Profile Icon Huang Xiao
Huang Xiao
Peng Xu Peng Xu
Author Profile Icon Peng Xu
Peng Xu
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Toc

Table of Contents (27) Chapters Close

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 26. Other Books You May Enjoy

Leveraging AI for UEBA

There were multiple heuristic techniques used to capture the patterns of user and entity behavior. However, the success of Artificial Intelligence (AI) in other problems of pattern recognition has motivated its use in computer security, particularly in UEBA. The idea is to use the data gathered in the SIEM tool, vectorize it, and define features that we will use to train a machine learning model. This model can be trained in an unsupervised manner, or we can also encode the domain knowledge of experts into labels to be able to use supervised machine learning methods.

In the following section, we will describe the common features and feature extraction techniques for UEBA.

UEBA features

The behavior of a user or entity can be characterized by extracting the following feature categories:

  • Access patterns: Usually, normal user accounts in a network are used to log in to a subset of physical or virtual devices. Furthermore, the use of privileges should...
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