DETECTION OF HTTP FLOOD ATTACKS BASED ON MACHINE LEARNING ALGORITHMS

Authors

Norbek Karimov, Tashkent region pedagogical skills center, Tashkent region, Uzbekistan. E-mail: norbekkarimov@mail.ru;Follow
Furkat Rakhmatov, Tashkent region pedagogical skills center, Tashkent region, Uzbekistan. E-mail: furkat.rahmatov@gmail.com;Follow
Oybek Xolmuminov, Tashkent University of Information Technologies named after Muhammad al-Khwarizmi. Address: Tashkent 100084, Amir Temur Avenue 108. Tashkent city, Uzbekistan. E-mail: xolmuminovoybek26@gmail.com.Follow

Abstract

This paper analyzes the effectiveness of Random Forest and SVM models for detecting HTTP Flood attacks. Experimental results demonstrate that both models achieve high accuracy. Evaluation was conducted using Precision, Recall, and F1 Score metrics. Additionally, key features of network traffic were extracted through correlation analysis to enable real-time application of the models in attack detection. The findings provide important insights into detecting DDoS attacks using machine learning and improving model performance.

First Page

68

Last Page

73

References

1. “O‘zbekiston Respublikasi kiberxavfsizligi - 2023 yil hisoboti”. https://csec.uz/uz/news/maqolalar/o-zbekiston-respublikasi-kiberxavfsizligi-2023-yil-hisoboti/
2. Aktualnye kiberugrozy: III kvartal 2024 [Current Cyber Threats: Q3 2024]. https://www.ptsecurity.com/ru-ru/research/analytics/aktualnye-kiberugrozy-iii-kvartal-2024-goda/#id1
3. Novye otchety po kiberatakam i sposobam ikh predotvrashcheniya [New reports on cyberattacks and methods of their prevention]. https://www.infowatch.ru/analytics/daydzhesty-i-obzory/novyye-otchety-po-kiberatakam-i-sposobam-ikh-predotvrascheniya
4. Significant Cyber Incidents. https://www.csis.org/programs/strategic-technologies-program/significant-cyber-incidents
5. Abbasi, M., Shahraki, A., Taherkordi, A. (2021). Deep learning for network traffic monitoring and analysis (NTMA): A survey. Computer Communications, 170, 19-41. https://doi.org/10.1016/j.comcom.2021.01.021.
6. Santoso, N. A., Lutfayza, R., Nughroho, B. I., Gunawan, G. (2024). Anomaly detection in network security systems using machine learning. Journal of Intelligent Decision Support System (IDSS), 7(2), 113-120. https://doi.org/10.35335/idss.v7i2.238.
7. Voruganti, N. K., Raju, S. K. (2023). Network Anomaly Detection Using a Random Forest Classifier. In Information and Knowledge Systems. https://doi.org/10.52305/XUNI4482.
8. Lei, Y. (2017). Network anomaly traffic detection algorithm based on SVM. In 2017 International Conference on Robots & Intelligent System (ICRIS). https://doi.org/10.1109/ICRIS.2017.61.
9. Liu, J., Liu, F., Ansari, N. (2014). Monitoring and analyzing big traffic data of a large-scale cellular network with Hadoop. IEEE Network, 28(4), 32-39.
10. Sivarajah, U., Kamal, M. M., Irani, Z., Weerakkody, V. (2017). Critical analysis of big data challenges and analytical methods. Journal of Business Research, 70, 263-286.
11. Zhou, D., Yan, Z., Fu, Y., Yao, Z. (2018). A survey on network data collection. Journal of Network and Computer Applications, 116, 9-23.
12. Lee, S., Levanti, K., Kim, H. S. (2014). Network monitoring: Present and future. Computer Networks, 65, 84-98.
13. Verma, S., Kawamoto, Y., Fadlullah, Z. M., Nishiyama, H., Kato, N. (2017). A survey on network methodologies for real-time analytics of massive IoT data and open research issues. IEEE Communications Surveys & Tutorials, 19(3), 1457-1477.
14. Lotfollahi, M., Siavoshani, M. J., Zade, R.S.H., Saberian, M. (2020). Deep packet: A novel approach for encrypted traffic classification using deep learning. Soft Computing, 24(3), 1999-2012.
15. Rahmatov, F. A., Xolmuminov, O. T. (2024). Tarmoq elektron resurslariga DDos hujumlari tahlili. Raqamli Transformatsiya va Sun’iy Intellekt, 2(2), 133-137.
16. Raxmatov, F. A. (2024). Veb-ilovalarga tahdidlar va himoya qilishning mavjud usullari tahlili. Management, Marketing and Finance, 1(2), 96-99.
17. Kolesnikov, A. V. (2015). Modelirovaniye setevogo trafika i algoritmy bor'by s peregruzkami na osnove metodov nelineynoy dinamiki i kratkosrochnogo prognozirovaniya vremennykh ryadov [Modeling of Network Traffic and Congestion Control Algorithms Based on Nonlinear Dynamics Methods and Short-Term Time Series Forecasting]. Dissertatsiya na soiskaniye uchenoy stepeni kandidata tekhnicheskikh nauk, Moskva. 144 p. (in Russina).
18. Htet, P.P., Thanda, S. (2019). Network traffic anomaly detection based on Apache Spark. In 2019 International Conference on Advanced Information Technologies (ICAIT). IEEE. 222-226
19. https://www.kaggle.com/datasets/oktayrdeki/ddos-traffic-dataset
20. Guthula, S., Battula, N., Beltiukov, R., Guo, W., Gupta, A. (2023). NetFound: Foundation model for network security. arXiv preprint arXiv:2310.17025. https://arxiv.org/abs/2310.17025
21. Alazab, M. (2022). A discrete time-varying greywolf IoT botnet detection system. Computer Communications, 192, 405-416.
22. Lo, W. W., Layeghy, S., Sarhan, M., Gallagher, M., Portmann, M. (2022). E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoT. In NOMS 2022 – IEEE/IFIP Network Operations and Management Symposium, Budapest, Hungary, 25-29 April. 1-9.
23. Aouedi, O. (2022). Machine learning-enabled network traffic analysis (Doctoral dissertation, Nantes Université). https://theses.hal.science/tel-03966012v2.
24. Alwhbi, I.A., Zou, C.C., Alharbi, R. N. (2024). Encrypted network traffic analysis and classification utilizing machine learning. Sensors, 24(11), 3509. https://doi.org/10.3390/s24113509.
25. Abbasi, M., Shahraki, A., Taherkordi, A. (2021). Deep learning for network traffic monitoring and analysis (NTMA): A survey. Computer Communications, 170, 19-41. https://doi.org/10.1016/j.comcom.2021.01.021.
26. Raspredelenie Puassona [Poisson distribution]. https://ru.wikipedia.org/wiki/Распределение_Пуассона
27. Raspredelenie Koshi [Cauchy distribution]. https://ru.wikipedia.org/wiki/Распределение_Коши
28. Hänsch, R. (2025). Handbook of Random Forests: Theory and Applications for Remote Sensing. 300 p. https://doi.org/10.1142/10552

Recommended Citation

Karimov, Norbek; Rakhmatov, Furkat; and Xolmuminov, Oybek (2025) "DETECTION OF HTTP FLOOD ATTACKS BASED ON MACHINE LEARNING ALGORITHMS," Chemical Technology, Control and Management: Vol. 2025: Iss. 2, Article 10.
DOI: https://doi.org/10.59048/2181-1105.1663