Towards Robust Network Security: Evaluating Machine Learning Algorithms for Intrusion Detection

Hafiz Burhan Ul Haq; Rabia Younis; Muhammad Shujat Ali

doi:10.31181/dma31202559

Authors

Hafiz Burhan Ul Haq Faculty of Computer Sciences Department of Information Technology, Lahore Garrison University, DHA phase 6, Lahore, 54000, Pakistan https://orcid.org/0000-0003-3026-3728
Rabia Younis Department of Information Technology, Faculty of Computer Science, Lahore Garrison University, Lahore, Pakistan https://orcid.org/0000-0003-1920-1809
Muhammad Shujat Ali Department of Information Technology, Faculty of Computer Science, Lahore Garrison University, Lahore, Pakistan https://orcid.org/0009-0005-8828-3053

DOI:

https://doi.org/10.31181/dma31202559

Keywords:

Network Security, Machine Learning, Intrusion Detection

Abstract

The constant growth of cyber threats has made network intrusion detection systems (NIDS) more crucial. Targeting anomalous behavior in a network is challenging because of the large number of features that exist. Consequently, the accuracy is affected, and there will be a greater chance of less reliability in the network. This study overcomes the limitations of traditional NIDS by using multiple machine learning (ML) algorithms to enhance intrusion detection capabilities. The efficacy of many ML algorithms in the context of NIDS is examined by paying special attention to their ability to detect intrusion based on features. Similarly, experimental analysis is conducted using a publicly available large dataset containing 41 features, whereby six algorithms were compared: AdaBoost, Gaussian Naive Bayes, Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors, and Multinomial Naive Bayes. Resultantly, SVM achieves the lowest accuracy at 53.15%. RF performs the best with a 99.78% accuracy rate. In addition, a comparative analysis is also performed, which is crucial for practitioners in the industry who want to implement effective NIDS.

Downloads

Download data is not yet available.

References

World Economic Forum. (2024). Global Cybersecurity Outlook 2024. Available at: https://www.weforum.org.

National University. (2024). 101 Cybersecurity Statistics and Trends for 2024. Available at: https://www.nu.edu.

Barreñada, L., Dhiman, P., Timmerman, D., Boulesteix, A. L., & Van Calster, B. (2024). Understanding overfitting in random forest for probability estimation: a visualization and simulation study. Diagnostic and Prognostic Research, 8(1), 14. https://doi.org/10.1186/s41512-024-00177-1.

Rustam, F., & Jurcut, A. D. (2024). Malicious traffic detection in multi-environment networks using novel S-DATE and PSO-D-SEM approaches. Computers & Security, 136, 103564. https://doi.org/10.1016/j.cose.2023.103564.

Susilo, B., & Sari, R. F. (2020). Intrusion detection in IoT networks using deep learning algorithm. Information, 11, 279. https://doi.org/10.3390/info11050279.

Asharf, J., Moustafa, N., Khurshid, H., Debie, E., Haider, W., & Wahab, A. (2020). A review of intrusion detection systems using machine and deep learning in internet of things: Challenges, solutions and future directions. Electronics, 9, 1177. https://doi.org/10.3390/electronics9071177.

Liu, L., Wang, P., Lin, J., & Liu, L. (2020). Intrusion detection of imbalanced network traffic based on machine learning and deep learning. IEEE Access, 9, 7550-7563. https://doi.org/10.1109/ACCESS.2020.3048198.

Sarker, H., Abushark, Y. B., Alsolami, F., & Khan, A. I. (2020). Intrudtree: A machine learning based cybersecurity intrusion detection model. Symmetry, 12, 754. https://doi.org/10.3390/sym12050754.

Dong, R. H., Li, X. Y., Zhang, Q. Y., & Yuan, H. (2020). Network intrusion detection model based on multivariate correlation analysis-long short-time memory network. IET Information Security, 14(2), 166-174. https://doi.org/10.1049/iet-ifs.2019.0294.

Injadat, M., Moubayed, A., Nassif, A. B., & Shami, A. (2020). Multi-stage optimized machine learning framework for network intrusion detection. IEEE Transactions on Network and Service Management, 18(2), 1803-1816. https://doi.org/10.1109/TNSM.2020.3014929.

Zhang, H., Li, Y., Lv, Z., Sangaiah, A. K., & Huang, T. (2020). A real-time and ubiquitous network attack detection based on deep belief network and support vector machine. IEEE/CAA Journal of Automatica Sinica, 7(3), 790-799. https://doi.org/10.1109/JAS.2020.1003099.

Özalp, A. N., & Albayrak, Z. (2022). Detecting cyber attacks with high-frequency features using machine learning algorithms. Acta Polytechnica Hungarica, 19(7), 213-233.

Talukder, M. A., Hasan, K. F., Islam, M. M., Uddin, M. A., Akhter, A., Yousuf, M. A., Alharbi, F., & Moni, M. A. (2023). A dependable hybrid machine learning model for network intrusion detection. Journal of Information Security and Applications, 72, 103405. https://doi.org/10.1016/j.jisa.2022.103405.

Chen, Z., Simsek, M., Kantarci, B., & Djukic, P. (2021). All predict wisest decides: A novel ensemble method to detect intrusive traffic in IoT networks. In 2021 IEEE Global Communications Conference, GLOBECOM (pp. 01–06). IEEE. https://doi.org/10.1109/GLOBECOM46510.2021.9685318.

Friha, O., Ferrag, M. A., Benbouzid, M., Berghout, T., Kantarci, B., & Choo, K.-K. R. (2023). 2DF IDS: Decentralized and differentially private federated learning-based intrusion detection system for industrial IoT. Computers & Security, 127, 103097. https://doi.org/10.1016/j.cose.2023.103097.

Kaggle. Network Intrusion Detection. Available at: https://www.kaggle.com/datasets/sampadab17/network-intrusion-detection/.

Chandrashekar, G., & Sahin, F. (2020). A survey on feature selection methods. Computers & Electrical Engineering, 40(1), 16-28. https://doi.org/10.1016/j.compeleceng.2013.11.024.

Hsu, C. W., & Lin, C. J. (2019). A comparison of methods for multi-class support vector machines. IEEE Transactions on Neural Networks and Learning Systems, 30(7), 1945-1958. https://doi.org/10.1109/72.991427.

Scikit-learn. AdaBoostClassifier. Available at: https://scikit-learn.org/dev/modules/generated/sklearn.ensemble.AdaBoostClassifier.html.

Sun, B., & Chen, H. (2021). A survey of k nearest neighbor algorithms for solving the class imbalanced problem. Wireless Communications and Mobile Computing, 2021, 5520990. https://doi.org/10.1155/2021/5520990.

Rish, I. (2001). An empirical study of the Naive Bayes classifier. In Handbook of Statistical Analysis and Data Mining Applications (pp. 305-315). Academic Press.

Abbas, M., Memon, K. A., Jamali, A. A., Memon, S., & Ahmed, A. (2019). Multinomial Naive Bayes classification model for sentiment analysis. IJCSNS International Journal of Computer Science and Network Security, 19(3), 62-67.