[
1. Ahmed, M., Mahmood, A.N. and Hu, J. (2016) A survey of network anomaly detection techniques. Journal of Network and Computer Applications, 60, 19–31.10.1016/j.jnca.2015.11.016
]Search in Google Scholar
[
2. Ahmad, B., Jian, W. and Ali, Z.A. (2018) Role of Machine Learning and Data Mining in Internet Security: Standing State with Future Directions. Journal of Computer Networks and Communications, Volume 2018, Article ID 6383145, Open access. DOI: 10.1155/2018/6383145.10.1155/2018/6383145
]Search in Google Scholar
[
3. Azevedo, G. (2022) Feature selection techniques for classification and Python tips for their application. In: Towards Data Science WEB site, https://towardsdatascience.com/feature-selection-techniques-for-classification-and-python-tips-for-their-application-10c0ddd7918b, [Accessed 04/02/2022].
]Search in Google Scholar
[
4. Binbusayyis, A., Vaiyapuri, T. (2019) Identifying and Benchmarking Key Features for Cyber Intrusion Detection: An Ensemble Approach. In: IEEE Access, July 2019, DOI: 10.1109/ACCESS.2019.2929487.10.1109/ACCESS.2019.2929487
]Search in Google Scholar
[
5. Brownlee, J. (2022) How to Choose a Feature Selection Method For Machine Learning. In: Machine Learning Mastery WEB site, https://machinelearningmastery.com/feature-selection-with-real-and-categorical-data/, [Accessed: 04/02/2022].
]Search in Google Scholar
[
6. Dasgupta, A. and Nath, A. (2016) Classification of Machine Learning Algorithms. International Journal of Innovative Research in Advanced Engineering (IJIRAE) ISSN: 2349-2763, 3(03).
]Search in Google Scholar
[
7. Dwivedi, S., Vardhan1, M., Tripathi, S. (2020) Incorporating evolutionary computation for securing wireless network against cyberthreats. The Journal of Supercomputing. Published online 20 Jan 2020. DOI:10.1007/s11227-020-03161-w10.1007/s11227-020-03161-w
]Search in Google Scholar
[
8. Garcia-Teodoro, P., Diaz-Verdejo, J., Macia-Fernandez, G. and Vazquez, E. (2009) Anomaly-based network intrusion detection: Techniques, systems and challenges. Computers and Security, 28(1–2), 18–28.10.1016/j.cose.2008.08.003
]Search in Google Scholar
[
9. Guyon, I., Elisseeff, A. (2003) An Introduction to Variable and Feature Selection. Journal of Machine Learning Research, 3, 1157-1182.
]Search in Google Scholar
[
10. Fawcett, T. (2005) An introduction to ROC analysis. Pattern Recognition Letters, 27(2006), 861–874. DOI:10.1016/j.patrec.2005.10.010. Available on line www.elsevier.com/locate/patrec10.1016/j.patrec.2005.10.010
]Search in Google Scholar
[
11. Faysel, M.A. and Haque, S. S. (2010) Towards Cyber Defense: Research in Intrusion Detection and Intrusion Prevention Systems. Journal of Computer Science, 10(7), 316–325.
]Search in Google Scholar
[
12. Ieracitano, C., Adeel, A., Morabito, F., Hussain, A. (2019) A Novel Statistical Analysis and Autoencoder Driven Intelligent Intrusion Detection Approach. Neurocomputing. https://www.sciencedirect.com/science/article/pii/S0925231219315759. DOI: 10.1016/j.neucom.2019.11.016.10.1016/j.neucom.2019.11.016
]Search in Google Scholar
[
13. Kamalakanta, E., Rupesh, S., Kumar, R., Padmalochan, Y., Madhav, V. (2020) A context-aware robust intrusion detection system: a reinforcement learning-based approach. International Journal of Information Security. DOI: 10.1007/s10207-019-00482-710.1007/s10207-019-00482-7
]Search in Google Scholar
[
14. Krivchenkov, A., Misnevs, B. and Grakovski, A. (2021a) Using Machine Learning for DoS Attacks Diagnostics. In: Reliability and Statistics in Transportation and Communication. RelStat 2020. Lecture Notes in Networks and Systems, 45–53. Springer.10.1007/978-3-030-68476-1_4
]Search in Google Scholar
[
15. Krivchenkov, A., Misnevs, B., Grakovski, A. (2021b) Experimental Comparison of ML/DL Approaches for Cyberattacks Diagnostics. In: Zamojski W. et al. DepCoS-RELCOMEX 2021, AISC, Springer, 1389, 213-223.10.1007/978-3-030-76773-0_21
]Search in Google Scholar
[
16. Krivchenkov, A., Misnevs, B. and Grakovski, A. (2022) Structural Analysis of the NSL-KDD Data Sets for Solving the Problem of Attacks Detection Using ML/DL Methods. In book: Reliability and Statistics in Transportation and Communication, RelStat 2021, 3-13. Springer.10.1007/978-3-030-96196-1_1
]Search in Google Scholar
[
17. Mohammad, A. (2021) Intrusion Detection Using a New Hybrid Feature Selection Model. Intelligent Automation & Soft Computing, 30(1). DOI:10.32604/iasc.2021.016140.10.32604/iasc.2021.016140
]Search in Google Scholar
[
18. Moustafa, N., Slay, J. (2015) UNSW-NB15: A Comprehensive Data set for Network Intrusion Detection systems. In: Military Communications and Information Systems Conference (MilCIS). Open access, https://www.researchgate.net/publication/287330529
]Search in Google Scholar
[
19. Moustafa, N. (2017) Designing an online and reliable statistical anomaly detection framework for dealing with large high-speed network traffic. Thesis for: PhD degree. Open access, https://www.researchgate.net/publication/328784548
]Search in Google Scholar
[
20. NSL-KDD. (2022) Network Security, Information Security, Cyber Security WEB site, https://www.kaggle.com/hassan06/nslkdd, [Accessed 2022/02/04].10.12968/S1353-4858(22)70024-4
]Search in Google Scholar
[
21. Raza, R., Ashfaq, He, Y., Chen, D. (2016) Toward an efficient fuzziness based instance selection methodology for intrusion detection system. Springer, published online 2016. DOI: 10.1007/s13042-016-0557-4.10.1007/s13042-016-0557-4
]Search in Google Scholar
[
22. Saaty, T. L. (1977) A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology, 15(3), 234–281.10.1016/0022-2496(77)90033-5
]Search in Google Scholar
[
23. Sathya, R. and Abraham, A. (2013) Comparison of Supervised and Unsupervised Learning Algorithms for Pattern Classification. International Journal of Advanced Research in Artificial Intelligence (IJARAI), 2(2).10.14569/IJARAI.2013.020206
]Search in Google Scholar
[
24. SNORT. Source: project “Snort” [Online]. https://www.snort.org/, [Accessed: 27/01/2022].
]Search in Google Scholar
[
25. Tan, Z., He, A., Nanda, P., Liu, R. (2014) A System for Denial-of-Service Attack Detection Based on Multivariate Correlation Analysis. In IEEE Transactions on Parallel and Distributed Systems, February 2014. DOI: 10.1109/TPDS.2013.14610.1109/TPDS.2013.146
]Search in Google Scholar
[
26. Tan, Z., Jamdagni, A., Hez, X., Nanda, P., Liu, R., Hu, J. (2015) Detection of Denial-of-Service Attacks Based on Computer Vision Techniques. In: IEEE Transactions on Computers, May 2015. DOI: 10.1109/TC.2014.2375218, https://www.researchgate.net/publication/26822572810.1109/TC.2014.2375218
]Search in Google Scholar
[
27. Tang, J., Alelyani, S. and Liu, H. (2015) Feature Selection for Classification: A Review. Published in: Data Classification: Algorithms and Applications. Open access, https://www.semanticscholar.org/paper/Feature-Selection-for-Classification%3A-A-Review-Tang-Alelyani/310ea531640728702fce6c743c1dd680a23d2ef4?p2df
]Search in Google Scholar
[
28. Zhou, Y., Cheng, G., Jiang, S. and Dai, M. (2015) An Efficient Intrusion Detection System Based on Feature Selection and Ense mble Classifier. Journal of LATEX class files, 14(8).
]Search in Google Scholar