ARCHIVES

1. Russell, S., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.
2. Sommer, R., & Paxson, V. (2019). Outside the Closed World: On Using Machine Learning for Network Intrusion Detection. IEEE
Symposium on Security and Privacy.
3. Garcia-Teodoro, P., Diaz-Verdejo, J., Macia-Fernandez, G., & Vazquez, E. (2021). Anomaly-Based Network Intrusion Detection. Computer
Security Journal, 28(1), 18–28.
4. Ahmed, M., Mahmood, A. N., & Hu, J. (2021). A Survey of Network Anomaly Detection Techniques. Journal of Network Security, 60(1),
19–31.
5. Kim, H., Park, S., & Lee, J. (2022). Deep Learning-Based Intrusion Detection Systems for Network Security. IEEE Access, 10, 45812–
45828.
6. Sharma, R., & Gupta, S. (2022). Artificial Intelligence for Cyber Threat Intelligence and Risk Assessment. International Journal of Cyber
Security, 14(2), 65–79.
7. Zhang, Y., & Wang, L. (2023). Real-Time Intrusion Detection Using Machine Learning Models. Journal of Information Security, 15(3),
101–115.
8. Patel, R., & Shah, M. (2023). AI-Enabled Cloud Security and Intrusion Detection Mechanisms. Cloud Computing Review, 9(4), 211–225.
9. Li, X., Chen, H., & Zhou, Y. (2024). Reinforcement Learning for Adaptive Cyber Defense Systems. Cybersecurity Research Journal, 11(1),
44–58.
10. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
11. Witten, I., Frank, E., Hall, M., & Pal, C. (2017). Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann.
12. Stallings, W. (2023). Network Security Essentials: Applications and Standards (7th ed.). Pearson.
13. Bishop, C. M. (2021). Pattern Recognition and Machine Learning. Springer.
14. Alpaydin, E. (2020). Introduction to Machine Learning. MIT Press.
15. Jain, A., & Singh, P. (2024). Artificial Intelligence Applications in Next-Generation Cyber Security Systems. International Journal of
Advanced Computing Research, 18(2), 87–102.