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Email Alert-Enabled Network Intrusion Detection Systems: A Supervised Machine Learning Approach with Recursive Feature Elimination
Subject area: Science,Engineering and Technology · Area of research: Computer Science
Abstract
In the evolving cybersecurity environment, the importance of a robust intrusion detection system (IDS) is paramount. This research explores the integration of supervised machine learning models such as decision trees, support vector machines (SVMs) and random forests to improve the capabilities of network intrusion detection systems (NIDS). The proposed methodology includes data pre-processing, feature selection and model training using the KDD-Cup99 dataset. This research presents a comparative analysis of the performance of a model with 41 features and a reduced set of 15 features obtained by recursive feature elimination (RFE). This research contributes to understanding the effectiveness of machine learning in strengthening email-alert enabled NIDS against cyber threats.
Keywords
Network intrusion, Supervised Machine learning, Network Attack detection, Network Security, Email-Alert, Threat Detection.
References
[1] S.M. Othman, F.M. Ba-Alwi, N.T. Alsohybe, and A. Y. Al-Hashida, "Intrusion detection model using machine learning algorithm on Big Data environment," J Big Data, vol. 5, no. 1, Dec. 2018, doi: 10.1186/s40537-018-0145-4.
[2] A. Khraisat, I. Gondal, P. Vamplew, and J. Kamruzzaman, "Survey of intrusion detection systems: techniques, datasets and challenges," Cybersecurity, vol. 2, no. 1, Dec. 2019, doi: 10.1186/s42400-019-0038-7.
[3] T. Rakshe, V. Jijabai, and V. Gonjari, "Anomaly based Network Intrusion Detection using Machine Learning Techniques." [Online]. Available: www.ijert.org
[4] Institute of Electrical and Electronics Engineers and PPG Institute of Technology, Proceedings of the 5th International Conference on Communication and Electronics Systems (ICCES 2020): 10-12, June 2020.
[5] M.A. Hossain and M.S. Islam, "Ensuring network security with a robust intrusion detection system using ensemble-based machine learning," Array, vol. 19, Sep. 2023, doi: 10.1016/j.array.2023.100306.
[6] J. Han and W. Pak, "High Performance Network Intrusion Detection System Using Two-Stage LSTM and Incremental Created Hybrid Features," Electronics (Switzerland), vol. 12, no. 4, Feb. 2023, doi: 10.3390/electronics12040956.
[7] M. Vishwakarma and N. Kesswani, "A new two-phase intrusion detection system with Naïve Bayes machine learning for data classification and elliptic envelop method for anomaly detection," Decision Analytics Journal, vol. 7, Jun. 2023, doi: 10.1016/j.dajour.2023.100233.
[8] L. Ashiku and C. Dagli, "Network Intrusion Detection System using Deep Learning," in Procedia Computer Science, Elsevier B.V., 2021, pp. 239–247. doi: 10.1016/j.procs.2021.05.025.
[9] Galgotias University. School of Computing Science and Engineering, Institute of Electrical and Electronics Engineers. Uttar Pradesh Section, and Institute of Electrical and Electronics Engineers, 2018 4th International Conference on Computing Communication and Automation (ICCCA).
[10] Institute of Electrical and Electronics Engineers, Proceedings, 2018 Sixteenth International Conference on ICT and Knowledge Engineering, November 21-23, 2018, Bangkok, Thailand.
How to cite this paper
@article{1705375,
author = {Mohammad Aman, Ashishika Singh, Tushar J Malviya, Aditi A Kalgi, Shreya Hegde; Harsh Kumar},
title = {Email Alert-Enabled Network Intrusion Detection Systems: A Supervised Machine Learning Approach with Recursive Feature Elimination},
journal = {Iconic Research And Engineering Journals},
year = {2024},
volume = {7},
number = {7},
pages = {110-118},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1705375.pdf},
abstract = {In the evolving cybersecurity environment, the importance of a robust intrusion detection system (IDS) is paramount. This research explores the integration of supervised machine learning models such as decision trees, support vector machines (SVMs) and random forests to improve the capabilities of network intrusion detection systems (NIDS). The proposed methodology includes data pre-processing, feature selection and model training using the KDD-Cup99 dataset. This research presents a comparative analysis of the performance of a model with 41 features and a reduced set of 15 features obtained by recursive feature elimination (RFE). This research contributes to understanding the effectiveness of machine learning in strengthening email-alert enabled NIDS against cyber threats.},
keywords = {Network intrusion, Supervised Machine learning, Network Attack detection, Network Security, Email-Alert, Threat Detection.},
month = {January},
}