Current - Issue
Year 2026 · Volume 5 · Issue 3
Original Article
Deep Digital Traffic Analytics for Cyber Threat Mitigation via Integrated Mining Architectures
Gowtham S. Nazre1
1 Assistant Professor, Department of Computer Applications Sri K. Puttaswamy First Grade College, Mysore, Karnataka, India.
Published Online: September-December 2026
Pages: 26-29
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20260503005
References
1. Sharafaldin, I., Lashkari, A. H., & Ghorbani, A. A., “Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization,” Proceedings of the 4th International Conference on Information Systems Security and Privacy (ICISSP), pp. 108-116, 2018.
2. Moustafa, N., & Slay, J., “UNSW-NB15: A Comprehensive Data Set for Network Intrusion Detection Systems (UNSW-NB15 dataset),” IEEE Military Communications Conference (MILCOM), pp. 1560-1565, 2015.
3. Buczak, A. L., & Guven, E., “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.
4. Sommer, R., & Paxson, V., “Outside the Closed World: On Using Machine Learning for Network Intrusion Detection,” IEEE Symposium on Security and Privacy (S&P), pp. 305-316, 2010.
5. Khaire, U. M., & Dhanalakshmi, R., “Stability of Feature Selection Algorithms: A Review,” Journal of King Saud University - Computer and Information Sciences, Vol. 34, No. 4, pp. 1060-1073, 2022.
6. Pajouh, H. H., Javidan, R., Khayami, R., Ali, D., & Choo, K. K. R., “A Two-Layer Dimension Reduction and Two-Tier Classification Model for Network Intrusion Detection System,” IEEE Transactions on Dependable and Secure Computing, Vol. 16, No. 5, pp. 803-815, 2019.
7. Tama, B. A., & Rhee, K. H., “An In-Depth Case Study of Anomaly Detection in Registry Traffic Using Ensemble Data Mining Methods,” IEEE Access, Vol. 7, pp. 38523-38537, 2019.
2. Moustafa, N., & Slay, J., “UNSW-NB15: A Comprehensive Data Set for Network Intrusion Detection Systems (UNSW-NB15 dataset),” IEEE Military Communications Conference (MILCOM), pp. 1560-1565, 2015.
3. Buczak, A. L., & Guven, E., “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.
4. Sommer, R., & Paxson, V., “Outside the Closed World: On Using Machine Learning for Network Intrusion Detection,” IEEE Symposium on Security and Privacy (S&P), pp. 305-316, 2010.
5. Khaire, U. M., & Dhanalakshmi, R., “Stability of Feature Selection Algorithms: A Review,” Journal of King Saud University - Computer and Information Sciences, Vol. 34, No. 4, pp. 1060-1073, 2022.
6. Pajouh, H. H., Javidan, R., Khayami, R., Ali, D., & Choo, K. K. R., “A Two-Layer Dimension Reduction and Two-Tier Classification Model for Network Intrusion Detection System,” IEEE Transactions on Dependable and Secure Computing, Vol. 16, No. 5, pp. 803-815, 2019.
7. Tama, B. A., & Rhee, K. H., “An In-Depth Case Study of Anomaly Detection in Registry Traffic Using Ensemble Data Mining Methods,” IEEE Access, Vol. 7, pp. 38523-38537, 2019.
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