Feature Selection Method for Ml/Dl Classification of Network Attacks in Digital Forensics

Grakovski, Alexander; Krivchenkov, Aleksandr; Misnevs, Boriss

doi:10.2478/ttj-2022-0011

Abstract

The research is related to machine learning and deep learning (ML/DL) methods for clustering and classification that are compatible with anomaly detection (network attacks detection) in digital forensics. Research is conducted in the field of selecting subsets of features of a dataset useful for constructing a good predictor (classifier). In this study, a new feature selection method for a classifier based on the Analytical Hierarchy Process (AHP) method is presented and tested. The proposed step-by-step algorithm for the iterative selection of these features makes it possible to obtain the minimum required list of features that are associated with attack events and can be used to detect them. For the classification, Artificial Neural Network (ANN) method is used. The accuracy of attack detection by the proposed method has been verified in numerical experiments.

References

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 Back to article
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 Back to article
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 Back to article
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 Back to article
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 Back to article
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 Back to article
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 Back to article
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 Back to article
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 Back to article
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 Back to article
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 Back to article
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 Back to article
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 Back to article
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 Back to article
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 Back to article
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 Back to article
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 Back to article
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 Back to article
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 Back to article
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 Back to article
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 Back to article
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 Back to article
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 Back to article
24. SNORT. Source: project “Snort” [Online]. https://www.snort.org/, [Accessed: 27/01/2022].
Search in Google Scholar Back to article
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 Back to article
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 Back to article
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 Back to article
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 Back to article

Feature Selection Method for Ml/Dl Classification of Network Attacks in Digital Forensics

Abstract

Paradigm

My account