<div>
	Enhanced anomaly detection in network security: a comprehensive ensemble approach</div>

Pandey Rashmikiran, Pandey Mrinal, Nazarov Alexey N.

2024 , VOLUME 24, NUMBER 5 ( september-october )

ISSN 2226-1494 (print), ISSN 2500-0373 (online)

Publications

Editor-in-Chief

Nikiforov
Vladimir O.
D.Sc., Prof.

Partners

doi: 10.17586/2226-1494-2024-24-5-788-796

Enhanced anomaly detection in network security: a comprehensive ensemble approach

R. Pandey, M. Pandey, A. Nazarov

Read the full article

Article in English

For citation:

Pandey R., Pandey M., Nazarov A.N. Enhanced anomaly detection in network security: a comprehensive ensemble approach. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2024, vol. 24, no. 5, pp. 788–796. doi: 10.17586/2226-1494-2024-24-5-788-796

Abstract

Detection and handling of anomalous behavior in the network systems are peremptory efforts to ensure security for vulnerable infrastructures amidst the dynamic context of cybersecurity. In this paper, we propose an ensemble machine learning model architecture that leverages the strengths of XGBoost, Gradient Boosting, Random Forest, and Support Vector Machine models to identify anomalies in the dataset. This method utilizes an ensemble of these models with weighted voting based on accuracy to enhance anomaly detection for robust and adaptive real-world network security. The proposed ensemble learning model is evaluated on standard metrics and demonstrates exceptional efficacy, achieving an impressive accuracy of 99.68 % on NSL KDD dataset. This remarkable performance extends the model prowess in discerning anomalies within network traffic showcasing its potential as a robust tool for enhancing cybersecurity measures against evolving threats.

Keywords: anomaly detection, bagging and boosting, ensemble approach, network security, neural network

References

Reichenbach M. New challenges in electronic payments. Intelligent Enterprises of the 21^st Century. Ed. by J.N.D. Gupta, S. Sharma. IGI Global, 2004, pp. 153–162. https://doi.org/10.4018/9781591401605.ch010
Kebande V.R., Karie N.M., Ikuesan R.A. Real-time monitoring as a supplementary security component of vigilantism in modern network environments. International Journal of Information Technology, 2021, vol. 13, no. 1, pp. 5–17. https://doi.org/10.1007/s41870-020-00585-8
Hareesh R., Senthil Kumar R.K., Kalluri R., Bindhumadhava B.S. Critical infrastructure asset discovery and monitoring for cyber security. Lecture Notes in Electrical Engineering, 2022, vol. 847, pp. 289–300. https://doi.org/10.1007/978-981-16-9008-2_27
Savage D., Zhang X., Yu X., Chou P., Wang Q. Anomaly detection in online social networks. Social Networks, 2014, vol. 39, pp. 62–70. https://doi.org/10.1016/j.socnet.2014.05.002
Benaddi H., Ibrahimi K., Benslimane A. Improving the intrusion detection system for NSL-KDD dataset based on PCA-fuzzy clustering-KNN. Proc. of the 6^th International Conference on Wireless Networks and Mobile Communications (WINCOM), 2018, pp. 1–6. https://doi.org/10.1109/wincom.2018.8629718
Su T., Sun H., Zhu J., Wang S., Li Y. BAT: Deep learning methods on network intrusion detection using NSL-KDD dataset. IEEE Access, 2020, vol. 8, pp. 29575–29585. https://doi.org/10.1109/access.2020.2972627
Wang C., Zhou H., Hao Z., Hu S., Li J., Zhang X., Jiang B., Chen X. Network traffic analysis over clustering-based collective anomaly detection. Computer Networks, 2022, vol. 205, pp. 108760. https://doi.org/10.1016/j.comnet.2022.108760
Keim Y., Mohapatra A.K. Cyber threat intelligence framework using advanced malware forensics. International Journal of Information Technology, 2022, vol. 14, no. 1, pp. 521–530. https://doi.org/10.1007/s41870-019-00280-3
XuB., Jang-Jaccard J., Singh A., Wei Y., Sabrina F. Improving performance of autoencoder-based network anomaly detection on nsl-kdd dataset. IEEE Access,2021, vol. 9, pp. 140136–140146. https://doi.org/10.1109/access.2021.3116612
Shone N., Ngoc T.N., Phai V.D., Shi Q. A deep learning approach to network intrusion detection. IEEE Transactions on Emerging Topics in Computational Intelligence, 2018, vol. 2, no. 1, pp. 41–50. https://doi.org/10.1109/tetci.2017.2772792
Sharma B., Sharma L., Lal C. Anomaly based network intrusion detection for IoT attacks using convolution neural network. Proc. of the IEEE 7^th International Conference for Convergence in Technology (I2CT), 2022, pp. 1–6. https://doi.org/10.1109/i2ct54291.2022.9824229
KrzemieńW., Jędrasiak K., Nawrat A. Anomaly detection in software defined networks using ensemble learning. Lecture Notes in Networks and Systems,2022, vol. 439, pp. 629–643.https://doi.org/10.1007/978-3-030-98015-3_44
Staudemeyer R.C. Applying long short-term memory recurrent neural networks to intrusion detection. South African Computer Journal, 2015, vol. 56, pp. 136–154. https://doi.org/10.18489/sacj.v56i1.248
Kim J., Kim J., Thu H.L.T., Kim H. Long short term memory recurrent neural network classifier for intrusion detection. Proc. of the International Conference on Platform Technology and Service (PlatCon), 2016, pp. 1–5. https://doi.org/10.1109/platcon.2016.7456805
Liu Z., Thapa N., Shaver A., Roy K., Yuan X., Khorsandroo S. Anomaly detection on IoT network intrusion using machine learning. Proc. of the International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (ICABCD), 2020, pp. 1–5. https://doi.org/10.1109/icabcd49160.2020.9183842
Khan W., Haroon M. An unsupervised deep learning ensemble model for anomaly detection in static attributed social networks. International Journal of Cognitive Computing in Engineering,2022, vol. 3, pp. 153–160. https://doi.org/10.1016/j.ijcce.2022.08.002
Gupta K., Sharma D.K., Gupta K.D., Kumar A. A tree classifier based network intrusion detection model for Internet of Medical Things. Computers and Electrical Engineering,2022, vol. 102, pp. 108158. https://doi.org/10.1016/j.compeleceng.2022.108158
Ma Q., Sun C., Cui B. A novel model for anomaly detection in network traffic based on support vector machine and clustering. Security and Communication Networks, 2021, pp. 170788. https://doi.org/10.1155/2021/2170788
Iliyasu A.S., Deng H. N-GAN: a novel anomaly-based network intrusion detection with generative adversarial networks. International Journal of Information Technology, 2022, vol. 14, no. 7, pp. 3365–3375. https://doi.org/10.1007/s41870-022-00910-3
Tavallaee M., Bagheri E., Lu W., Ghorbani A.A. A detailed analysis of the KDD CUP 99 data set. Proc. of the IEEE Symposium on Computational Intelligence for Security and Defense Applications, 2009, pp. 1–6. https://doi.org/10.1109/cisda.2009.5356528
Panesar A. Evaluating machine learning models. Machine Learning and AI for Healthcare. Apress, Berkeley, CA, 2021, pp. 189–205. https://doi.org/10.1007/978-1-4842-6537-6_7
Assy A.T., Mostafa Y., Abd El-khaleq A., Mashaly M. Anomaly-based intrusion detection system using one-dimensional convolutional neural network. Procedia Computer Science, 2023, vol. 220, pp. 78–85. https://doi.org/10.1016/j.procs.2023.03.013
Acharya T., Annamalai A., Chouikha M.F. Efficacy of bidirectional LSTM model for network-based anomaly detection. Proc. of the IEEE 13^th Symposium on Computer Applications & Industrial Electronics (ISCAIE), 2023, pp. 336–341. https://doi.org/10.1109/iscaie57739.2023.10165336
Kavitha S., Uma Maheswari N., Venkatesh R. Network anomaly detection for NSL-KDD dataset using deep learning. Information Technology in Industry, 2021, vol. 9, no. 2, pp. 821–827. https://doi.org/10.17762/itii.v9i2.419
Gadal S., Mokhtar R., Abdelhaq M., Alsaqour R., Ali E.S., Saeed R. Machine learning-based anomaly detection using K-mean array and sequential minimal optimization. Electronics, 2022, vol. 11, no. 14, pp. 2158. https://doi.org/10.3390/electronics11142158

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License