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

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

References

Brückner, M., Scheffer, T., 2009. Nash Equilibria of Static Prediction Games. Presented at the Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference, pp. 171–179.
Carlini, N., Nasr, M., Choquette-Choo, C.A., Jagielski, M., Gao, I., Awadalla, A., Koh, P.W., Ippolito, D., Lee, K., Tramer, F., Schmidt, L., 2023. Are aligned neural networks adversarially aligned? https://doi.org/10.48550/arXiv.2306.15447
Nguyen, A., Yosinski, J., Clune, J., 2015. Deep neural networks are easily fooled: High confidence predictions for unrecognizable images, in: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Presented at the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 427–436. https://doi.org/10.1109/CVPR.2015.7298640
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R., 2014. Intriguing properties of neural networks. https://doi...