ARCHIVES
Original Article
AI-Driven Network Threat Detection Using Synthetic Traffic Analysis
Syed Abdul Jamaal1
Dr. Khaja Mahabubullah2
1Student, MCA Deccan College of Engineering and Technology, Hyderabad, Telangana, India. 2Professor & HOD, MCA Deccan College of Engineering and Technology, Hyderabad, Telangana, India.
Published Online: September-December 2025
Pages: 17-21
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20250403004
References
1. R. Sommer and V. Paxson, “Outside the closed world: On using machine learning for network intrusion detection,” 2010 IEEE Symposium on Security and Privacy, pp. 305–316, 2010. doi: 10.1109/SP.2010.25.
2. 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.
3. Y. Zhang, L. Wang, and Y. Wang, “Anomaly detection in network traffic based on deep learning,” Security and Communication Networks, 2021. doi: 10.1155/2021/5591728.
4. W. C. Lin, S. W. Ke, and C. F. Tsai, “CANN: An intrusion detection system based on combining cluster centers and nearest neighbors,” Knowledge-Based Systems, vol. 78, pp. 13–21, 2015. doi: 10.1016/j.knosys.2015.01.009.
5. M. O. Shafiq, X. Yu, Z. Gui, and A. K. Bashir, “Adaptive intrusion detection using machine learning algorithms: A performance comparison,” Electronics, vol. 9, no. 1, p. 40, 2020. doi: 10.3390/electronics9010040.
6. R. D. Camino, J. M. Jiménez, and E. J. Fonseca, “Generating synthetic data for intrusion detection systems,” Computers & Security, vol. 95, p. 101891, 2020. doi: 10.1016/j.cose.2020.101891.
7. G. Kim, S. Lee, and S. Kim, “A novel hybrid intrusion detection method integrating anomaly detection with misuse detection,” Expert Systems with Applications, vol. 41, no. 4, pp. 1690–1700, 2014. doi: 10.1016/j.eswa.2013.08.066.
8. V. Lemaire, A. Plantec, and A. Bondu, “Synthetic data for machine learning in cybersecurity: Survey and use cases,” arXiv preprint arXiv:2103.10248, 2021. Available: https://arxiv.org/abs/2103.10248.
9. Y. Mirsky, T. Doitshman, Y. Elovici, and Y. Shabtai, “Kitsune: An ensemble of autoencoders for online network intrusion detection,” Network and Distributed System Security Symposium (NDSS), 2018. Available: https://www.ndss-symposium.org/ndss2018/ndss-2018-programme/kitsune-ensemble-autoencoders-online-network-intrusion-detection/.
10. G. Creech and J. Hu, “A semantic approach to host-based intrusion detection systems using contiguous and discontiguous system call patterns,” IEEE Transactions on Computers, vol. 63, no. 4, pp. 807–819, 2014. doi: 10.1109/TC.2013.13
2. 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.
3. Y. Zhang, L. Wang, and Y. Wang, “Anomaly detection in network traffic based on deep learning,” Security and Communication Networks, 2021. doi: 10.1155/2021/5591728.
4. W. C. Lin, S. W. Ke, and C. F. Tsai, “CANN: An intrusion detection system based on combining cluster centers and nearest neighbors,” Knowledge-Based Systems, vol. 78, pp. 13–21, 2015. doi: 10.1016/j.knosys.2015.01.009.
5. M. O. Shafiq, X. Yu, Z. Gui, and A. K. Bashir, “Adaptive intrusion detection using machine learning algorithms: A performance comparison,” Electronics, vol. 9, no. 1, p. 40, 2020. doi: 10.3390/electronics9010040.
6. R. D. Camino, J. M. Jiménez, and E. J. Fonseca, “Generating synthetic data for intrusion detection systems,” Computers & Security, vol. 95, p. 101891, 2020. doi: 10.1016/j.cose.2020.101891.
7. G. Kim, S. Lee, and S. Kim, “A novel hybrid intrusion detection method integrating anomaly detection with misuse detection,” Expert Systems with Applications, vol. 41, no. 4, pp. 1690–1700, 2014. doi: 10.1016/j.eswa.2013.08.066.
8. V. Lemaire, A. Plantec, and A. Bondu, “Synthetic data for machine learning in cybersecurity: Survey and use cases,” arXiv preprint arXiv:2103.10248, 2021. Available: https://arxiv.org/abs/2103.10248.
9. Y. Mirsky, T. Doitshman, Y. Elovici, and Y. Shabtai, “Kitsune: An ensemble of autoencoders for online network intrusion detection,” Network and Distributed System Security Symposium (NDSS), 2018. Available: https://www.ndss-symposium.org/ndss2018/ndss-2018-programme/kitsune-ensemble-autoencoders-online-network-intrusion-detection/.
10. G. Creech and J. Hu, “A semantic approach to host-based intrusion detection systems using contiguous and discontiguous system call patterns,” IEEE Transactions on Computers, vol. 63, no. 4, pp. 807–819, 2014. doi: 10.1109/TC.2013.13
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