ARCHIVES
Year 2026 · Volume 5 · Issue 2
Original Article
AI-Driven Threat Detection in Cloud Infrastructure: An Intelligent Security Monitoring Framework
Rishu Kumar1
Akhila S Babu2
1 2 School of Science and Computer Studies, CMR University, Bengaluru, Karnataka, India.
Published Online: May-August 2026
Pages: 1043-1047
Cite this article
No DOIReferences
1. P. Mell and T. Grance, "The NIST Definition of Cloud Computing," National Institute of Standards and Technology, 2011.
2. R. Sommer and V. Paxson, "Outside the Closed World: On Using Machine Learning for Network Intrusion Detection," in Proc. IEEE Symposium on Security and Privacy, 2010, pp. 305–316.
3. I. H. Sarker, "AI-Based Cybersecurity Frameworks for Cloud Computing," IEEE Access, vol. 9, pp. 120719–120743, 2021.
4. V. Chandola, A. Banerjee, and V. Kumar, "Anomaly Detection: A Survey," ACM Computing Surveys, vol. 41, no. 3, 2009.
5. I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, MIT Press, 2016.
6. M. Tavallaee et al., "A Detailed Analysis of the KDD Cup 99 Dataset," in Proc. IEEE CISDA, 2009.
7. M. Sharafaldin, A. H. Lashkari, and A. A. Ghorbani, "Toward Generating a New Intrusion Detection Dataset," in Proc. ICISSP, 2018.
8. Cloud Security Alliance, "Top Threats to Cloud Computing," 2020.
9. E. Alpaydin, Introduction to Machine Learning, MIT Press, 2020.
10. S. Dua and X. Du, Data Mining and Machine Learning in Cybersecurity, CRC Press, 2016.
11. D. E. Denning, "An Intrusion-Detection Model," IEEE Trans. Software Engineering, vol. 13, no. 2, pp. 222–232, 1987.
12. J. McHugh, "Testing Intrusion Detection Systems," ACM TISSEC, vol. 3, no. 4, pp. 262–294, 2000.
13. T. M. Mitchell, Machine Learning, McGraw-Hill, 1997.
14. C. Bishop, Pattern Recognition and Machine Learning, Springer, 2006.
15. K. Scarfone and P. Mell, "Guide to Intrusion Detection and Prevention Systems," NIST, 2007.
16. W. Lee and S. J. Stolfo, "Data Mining Approaches for Intrusion Detection," in Proc. USENIX Security Symposium, 1998.
17. G. Creech and J. Hu, "A Semantic Approach to Host-Based Intrusion Detection," IEEE Trans. Computers, 2014.
18. N. Moustafa and J. Slay, "UNSW-NB15 Dataset," in Proc. MilCIS, 2015.
19. S. Axelsson, "The Base-Rate Fallacy and IDS," ACM CCS, 1999.
20. A. Patcha and J. Park, "An Overview of Anomaly Detection Techniques," Computer Networks, vol. 51, 2007.
21. L. Breiman, "Random Forests," Machine Learning, vol. 45, no. 1, pp. 5–32, 2001.
22. V. Vapnik, The Nature of Statistical Learning Theory, Springer, 1995.
23. J. Friedman, "Greedy Function Approximation: Gradient Boosting Machine," Annals of Statistics, 2001.
24. T. Chen and C. Guestrin, "XGBoost: A Scalable Tree Boosting System," in Proc. KDD, 2016.
25. Y. LeCun, Y. Bengio, and G. Hinton, "Deep Learning," Nature, vol. 521, pp. 436–444, 2015.
26. H. Kim et al., "Deep Learning-Based Intrusion Detection," IEEE Access, 2016.
27. M. Ring et al., "A Survey of Network-Based Intrusion Detection Data Sets," Computers & Security, 2019.
28. A. Javaid et al., "A Deep Learning Approach for Network Intrusion Detection," Procedia Computer Science, 2016.
29. Z. Zhang et al., "Network Anomaly Detection Using Deep Learning," IEEE Systems Journal, 2019.
30. S. Yu et al., "Cloud Security Issues and Challenges," IEEE Network, 2015.
31. K. Modi et al., "A Survey of Intrusion Detection Techniques in Cloud," Journal of Network and Computer Applications, 2013.
32. S. Subashini and V. Kavitha, "A Survey on Security Issues in Cloud Computing," Journal of Network and Computer Applications, 2011.
33. N. Gruschka and L. Lo Iacono, "Vulnerable Cloud: SOAP Message Security Validation Revisited," IEEE Int. Conf. Web Services, 2009.
2. R. Sommer and V. Paxson, "Outside the Closed World: On Using Machine Learning for Network Intrusion Detection," in Proc. IEEE Symposium on Security and Privacy, 2010, pp. 305–316.
3. I. H. Sarker, "AI-Based Cybersecurity Frameworks for Cloud Computing," IEEE Access, vol. 9, pp. 120719–120743, 2021.
4. V. Chandola, A. Banerjee, and V. Kumar, "Anomaly Detection: A Survey," ACM Computing Surveys, vol. 41, no. 3, 2009.
5. I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, MIT Press, 2016.
6. M. Tavallaee et al., "A Detailed Analysis of the KDD Cup 99 Dataset," in Proc. IEEE CISDA, 2009.
7. M. Sharafaldin, A. H. Lashkari, and A. A. Ghorbani, "Toward Generating a New Intrusion Detection Dataset," in Proc. ICISSP, 2018.
8. Cloud Security Alliance, "Top Threats to Cloud Computing," 2020.
9. E. Alpaydin, Introduction to Machine Learning, MIT Press, 2020.
10. S. Dua and X. Du, Data Mining and Machine Learning in Cybersecurity, CRC Press, 2016.
11. D. E. Denning, "An Intrusion-Detection Model," IEEE Trans. Software Engineering, vol. 13, no. 2, pp. 222–232, 1987.
12. J. McHugh, "Testing Intrusion Detection Systems," ACM TISSEC, vol. 3, no. 4, pp. 262–294, 2000.
13. T. M. Mitchell, Machine Learning, McGraw-Hill, 1997.
14. C. Bishop, Pattern Recognition and Machine Learning, Springer, 2006.
15. K. Scarfone and P. Mell, "Guide to Intrusion Detection and Prevention Systems," NIST, 2007.
16. W. Lee and S. J. Stolfo, "Data Mining Approaches for Intrusion Detection," in Proc. USENIX Security Symposium, 1998.
17. G. Creech and J. Hu, "A Semantic Approach to Host-Based Intrusion Detection," IEEE Trans. Computers, 2014.
18. N. Moustafa and J. Slay, "UNSW-NB15 Dataset," in Proc. MilCIS, 2015.
19. S. Axelsson, "The Base-Rate Fallacy and IDS," ACM CCS, 1999.
20. A. Patcha and J. Park, "An Overview of Anomaly Detection Techniques," Computer Networks, vol. 51, 2007.
21. L. Breiman, "Random Forests," Machine Learning, vol. 45, no. 1, pp. 5–32, 2001.
22. V. Vapnik, The Nature of Statistical Learning Theory, Springer, 1995.
23. J. Friedman, "Greedy Function Approximation: Gradient Boosting Machine," Annals of Statistics, 2001.
24. T. Chen and C. Guestrin, "XGBoost: A Scalable Tree Boosting System," in Proc. KDD, 2016.
25. Y. LeCun, Y. Bengio, and G. Hinton, "Deep Learning," Nature, vol. 521, pp. 436–444, 2015.
26. H. Kim et al., "Deep Learning-Based Intrusion Detection," IEEE Access, 2016.
27. M. Ring et al., "A Survey of Network-Based Intrusion Detection Data Sets," Computers & Security, 2019.
28. A. Javaid et al., "A Deep Learning Approach for Network Intrusion Detection," Procedia Computer Science, 2016.
29. Z. Zhang et al., "Network Anomaly Detection Using Deep Learning," IEEE Systems Journal, 2019.
30. S. Yu et al., "Cloud Security Issues and Challenges," IEEE Network, 2015.
31. K. Modi et al., "A Survey of Intrusion Detection Techniques in Cloud," Journal of Network and Computer Applications, 2013.
32. S. Subashini and V. Kavitha, "A Survey on Security Issues in Cloud Computing," Journal of Network and Computer Applications, 2011.
33. N. Gruschka and L. Lo Iacono, "Vulnerable Cloud: SOAP Message Security Validation Revisited," IEEE Int. Conf. Web Services, 2009.
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