ARCHIVES

1. P. Garcia-Teodoro, J. Diaz-Verdejo, G. Macia-Fernandez, and E. Vazquez, “Anomaly-based network intrusion detection: Techniques, systems and challenges,” Computers & Security, vol. 28, no. 1–2, pp. 18–28, 2009. https://doi.org/10.1016/j.cose.2008.08.003
2. N. Moustafa and J. Slay, “UNSW-NB15: A comprehensive data set for network intrusion detection systems,” in Military Communications and Information Systems Conference (MilCIS), IEEE, 2015. https://doi.org/10.1109/MilCIS.2015.7348942
3. 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. https://doi.org/10.1109/TETCI.2017.2772792
4. 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. https://doi.org/10.1016/j.eswa.2013.08.066
5. Scikit-learn Developers, Scikit-learn: Machine Learning in Python, 2024. [Online]. Available: https://scikit-learn.org/
6. Wireshark Foundation, Wireshark Network Protocol Analyzer, 2024. [Online]. Available: https://www.wireshark.org/
7. Scapy Project, Scapy: Packet Manipulation Tool for Python, 2024. [Online]. Available: https://scapy.net/
8. TensorFlow Developers, TensorFlow: An end-to-end open-source platform for machine learning, 2024. [Online]. Available: https://www.tensorflow.org/
9. NIST, NIST Special Publication 800-94 Rev.1: Guide to Intrusion Detection and Prevention Systems (IDPS), 2020. [Online]. Available: https://csrc.nist.gov/publications/detail/sp/800-94/rev-1/draft
10. J. Zhang and M. Zulkernine, “Anomaly based network intrusion detection with unsupervised outlier detection,” in Proc. IEEE International Conference on Communications (ICC), 2006. https://doi.org/10.1109/ICC.2006.255052