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An adaptive machine learning framework for early detection of emerging cyber threats using network traffic analysis
Authors
1 2 3 Assistant Professor, Department of Computer Science & IT, Srinivasan College of Arts and Science, Perambalur, Tamil Nadu, India.
Article Information
- Published In
- Volume 5, Issue 3 (2026)
- Published Online
- September-December 2026
- Pages
- 780-786
- Article Type
- Original Article
- Check for updates
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References
1. Prof. A. Mohamed Azharudheen, Mrs. A. Kumudham, & Ms. S. Kalaivani. “A Scalable Deep Learning-Optimized Data Security Architecture for High-Availability Big Data Environments.” International Journal of Engineering Research & Technology (IJERT), ISSN 2278-0181, Vol. 14, Issue 12, December 9, 2025. DOI: 10.17577/IJERTV14IS120127.
2. A. Mohamed Azharudheen and Dr.V. Vijayalakshmi, “Privacy-Preserving Data Protection: A Novel Mechanism for Maximizing Availability Without Compromising Confidentiality,” Journal of Information System Engineering and Management, E-ISSN: 2468-4376, Volume.10 Issue.21, 2025.
3. A. Mohamed Azharudheen and Dr.V. Vijayalakshmi, “Improvement of data analysis and protection using novel privacy-preserving methods for big data application” The Scientific Temper Vol. 15, no. 2, pp. 2181-2189, 2024.
4. A. Mohamed Azharudheen and Dr.V. Vijayalakshmi, “Analyze the New Data Protection Mechanism to Maximize Data Availability without Having Compromise Data Privacy” Educational Administration: Theory and Practice, Vol.30. No.5, pp. 3911-3922, 2024.
5. Prof. A. Mohamed Azharudheen, Kavibharathi S, “A Hybrid Deep Learning–Driven Privacy Preservation Framework for High-Availability Big Data System”, Technische Sicherheit (Technical Scurity) Journal, Volume 26, Issue 1, January 2026, Pages 1-16.
6. Sharin N. & Anitha K. “Automated Railway Track Defect Detection System using YOLO Deep Learning Algorithm.” International Journal of Engineering Research & Technology (IJERT), ISSN 2278-0181, Vol. 14, Issue 12, December 2025. DOI: 10.17577/IJERTV14IS120242.
7. Anitha K., Sharin N., & Madhumitha R. “Artificial Intelligence Powered Credit Card Fraud Detection System using Random Forest Feature Selection with Convolution Neural Network.” International Journal of Engineering Research & Technology (IJERT), ISSN 2278-0181, Vol. 14, Issue 12, December 2025. DOI: 10.17577/IJERTV14IS120237.
8. A. L. Buczak and E. Guven, “A survey of data mining and machine learning methods for cyber security intrusion detection,” IEEE Communications Surveys & Tutorials, vol. 18, no. 2, pp. 1153–1176, 2016. DOI: 10.1109/COMST.2015.2494502.
9. Z. Ahmad, A. S. Khan, C. W. Shiang, J. Abdullah, and F. Ahmad, “Network intrusion detection system: A systematic study of machine learning and deep learning approaches,” Transactions on Emerging Telecommunications Technologies, vol. 32, no. 1, 2021. DOI: 10.1002/ett.4150.
10. M. Ahmed, A. N. Mahmood, and J. Hu, “A survey of network anomaly detection techniques,” Journal of Network and Computer Applications, vol. 60, pp. 19–31, 2016. DOI: 10.1016/j.jnca.2015.11.016.
11. “Machine learning techniques for network-based intrusion detection system: A survey paper,” in Proceedings of the 2021 National Computing Colleges Conference (NCCC), 2021. DOI: 10.1109/NCCC49330.2021.9428827.
12. R. Sommer and V. Paxson, “Outside the closed world: On using machine learning for network intrusion detection,” in Proceedings of the IEEE Symposium on Security and Privacy, 2010, pp. 305–316. DOI: 10.1109/SP.2010.25.
13. J. Gama, I. Žliobaitė, A. Bifet, M. Pechenizkiy, and A. Bouchachia, “A survey on concept drift adaptation,” ACM Computing Surveys, vol. 46, no. 4, Article 44, 2014. DOI: 10.1145/2523813.
14. F. M. Martins et al., “Adaptive online learning approaches for detecting unknown attacks in network traffic: A systematic literature review,” Security and Privacy, 2025. DOI: 10.1002/spy2.70084.
15. [15] A. L. Buczak and E. Guven, “A survey of data mining and machine learning methods for cyber security intrusion detection,” IEEE Communications Surveys & Tutorials, vol. 18, no. 2, pp. 1153–1176, 2016. DOI: 10.1109/COMST.2015.2494502.
16. [16] N. Moustafa and J. Slay, “UNSW-NB15: A comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set),” in Proceedings of the Military Communications and Information Systems Conference (MilCIS), 2015, pp. 1–6. DOI: 10.1109/MilCIS.2015.7348942.
17. [17] I. Sharafaldin, A. H. Lashkari, and A. A. Ghorbani, “Toward generating a new intrusion detection dataset and intrusion traffic characterization,” in Proceedings of the International Conference on Information Systems Security and Privacy (ICISSP), 2018, pp. 108–116. DOI: 10.5220/0006639801080116.
18. [18] M. Ring, S. Wunderlich, D. Scheuring, D. Landes, and A. Hotho, “A survey of network-based intrusion detection data sets,” Computers & Security, vol. 86, pp. 147–167, 2019. DOI: 10.1016/j.cose.2019.06.005.
19. [19] N. Shone, T. N. Ngoc, V. D. Phai, and Q. Shi, “A deep learning approach to network intrusion detection,” IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 2, no. 1, pp. 41–50, 2018.
20. [20] A. Javaid, Q. Niyaz, W. Sun, and M. Alam, “A deep learning approach for network intrusion detection system,” in Proceedings of the 9th EAI International Conference on Bio-inspired Information and Communications Technologies, 2016.
21. [21] Z. Ahmad et al., “Anomaly detection using deep neural network for IoT architecture,” Applied Sciences, vol. 11, no. 15, Article 7050, 2021. DOI: 10.3390/app11157050
2. A. Mohamed Azharudheen and Dr.V. Vijayalakshmi, “Privacy-Preserving Data Protection: A Novel Mechanism for Maximizing Availability Without Compromising Confidentiality,” Journal of Information System Engineering and Management, E-ISSN: 2468-4376, Volume.10 Issue.21, 2025.
3. A. Mohamed Azharudheen and Dr.V. Vijayalakshmi, “Improvement of data analysis and protection using novel privacy-preserving methods for big data application” The Scientific Temper Vol. 15, no. 2, pp. 2181-2189, 2024.
4. A. Mohamed Azharudheen and Dr.V. Vijayalakshmi, “Analyze the New Data Protection Mechanism to Maximize Data Availability without Having Compromise Data Privacy” Educational Administration: Theory and Practice, Vol.30. No.5, pp. 3911-3922, 2024.
5. Prof. A. Mohamed Azharudheen, Kavibharathi S, “A Hybrid Deep Learning–Driven Privacy Preservation Framework for High-Availability Big Data System”, Technische Sicherheit (Technical Scurity) Journal, Volume 26, Issue 1, January 2026, Pages 1-16.
6. Sharin N. & Anitha K. “Automated Railway Track Defect Detection System using YOLO Deep Learning Algorithm.” International Journal of Engineering Research & Technology (IJERT), ISSN 2278-0181, Vol. 14, Issue 12, December 2025. DOI: 10.17577/IJERTV14IS120242.
7. Anitha K., Sharin N., & Madhumitha R. “Artificial Intelligence Powered Credit Card Fraud Detection System using Random Forest Feature Selection with Convolution Neural Network.” International Journal of Engineering Research & Technology (IJERT), ISSN 2278-0181, Vol. 14, Issue 12, December 2025. DOI: 10.17577/IJERTV14IS120237.
8. A. L. Buczak and E. Guven, “A survey of data mining and machine learning methods for cyber security intrusion detection,” IEEE Communications Surveys & Tutorials, vol. 18, no. 2, pp. 1153–1176, 2016. DOI: 10.1109/COMST.2015.2494502.
9. Z. Ahmad, A. S. Khan, C. W. Shiang, J. Abdullah, and F. Ahmad, “Network intrusion detection system: A systematic study of machine learning and deep learning approaches,” Transactions on Emerging Telecommunications Technologies, vol. 32, no. 1, 2021. DOI: 10.1002/ett.4150.
10. M. Ahmed, A. N. Mahmood, and J. Hu, “A survey of network anomaly detection techniques,” Journal of Network and Computer Applications, vol. 60, pp. 19–31, 2016. DOI: 10.1016/j.jnca.2015.11.016.
11. “Machine learning techniques for network-based intrusion detection system: A survey paper,” in Proceedings of the 2021 National Computing Colleges Conference (NCCC), 2021. DOI: 10.1109/NCCC49330.2021.9428827.
12. R. Sommer and V. Paxson, “Outside the closed world: On using machine learning for network intrusion detection,” in Proceedings of the IEEE Symposium on Security and Privacy, 2010, pp. 305–316. DOI: 10.1109/SP.2010.25.
13. J. Gama, I. Žliobaitė, A. Bifet, M. Pechenizkiy, and A. Bouchachia, “A survey on concept drift adaptation,” ACM Computing Surveys, vol. 46, no. 4, Article 44, 2014. DOI: 10.1145/2523813.
14. F. M. Martins et al., “Adaptive online learning approaches for detecting unknown attacks in network traffic: A systematic literature review,” Security and Privacy, 2025. DOI: 10.1002/spy2.70084.
15. [15] A. L. Buczak and E. Guven, “A survey of data mining and machine learning methods for cyber security intrusion detection,” IEEE Communications Surveys & Tutorials, vol. 18, no. 2, pp. 1153–1176, 2016. DOI: 10.1109/COMST.2015.2494502.
16. [16] N. Moustafa and J. Slay, “UNSW-NB15: A comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set),” in Proceedings of the Military Communications and Information Systems Conference (MilCIS), 2015, pp. 1–6. DOI: 10.1109/MilCIS.2015.7348942.
17. [17] I. Sharafaldin, A. H. Lashkari, and A. A. Ghorbani, “Toward generating a new intrusion detection dataset and intrusion traffic characterization,” in Proceedings of the International Conference on Information Systems Security and Privacy (ICISSP), 2018, pp. 108–116. DOI: 10.5220/0006639801080116.
18. [18] M. Ring, S. Wunderlich, D. Scheuring, D. Landes, and A. Hotho, “A survey of network-based intrusion detection data sets,” Computers & Security, vol. 86, pp. 147–167, 2019. DOI: 10.1016/j.cose.2019.06.005.
19. [19] N. Shone, T. N. Ngoc, V. D. Phai, and Q. Shi, “A deep learning approach to network intrusion detection,” IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 2, no. 1, pp. 41–50, 2018.
20. [20] A. Javaid, Q. Niyaz, W. Sun, and M. Alam, “A deep learning approach for network intrusion detection system,” in Proceedings of the 9th EAI International Conference on Bio-inspired Information and Communications Technologies, 2016.
21. [21] Z. Ahmad et al., “Anomaly detection using deep neural network for IoT architecture,” Applied Sciences, vol. 11, no. 15, Article 7050, 2021. DOI: 10.3390/app11157050
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