IIARD International Institute of Academic Research and Development

INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND MATHEMATICAL THEORY (IJCSMT )

E-ISSN 2545-5699
P-ISSN 2695-1924
VOL. 11 NO. 2 2025
DOI: 10.56201/ijcsmt.v11.no2.2025.pg26.39

---

A Model for the Detection of Denial-of-Service Attack Patterns

Bennett, E O, Nwala, Q N

---

Abstract

Denial of Service (DoS) attacks is a critical cybersecurity threat that can disrupt network services by overwhelming it with illegitimate traffic. This paper presents a hybrid detection and mitigation system to address DoS attacks. The system leverages Python as the primary programming language, incorporating its robust ecosystem of libraries such as Scikit-learn, TensorFlow, and Tkinter for machine learning, feature extraction, and GUI development. The system integrates a hybrid model combining K-means clustering with a Random Forest classifier. Initially, the K- means algorithm groups data into clusters, which are then fed into the Random Forest for classification. The system was trained and evaluated using an undersampled DoS dataset to ensure balanced class representation. Results show that the model performed quite excellently, achieving a 98.97% accuracy in detecting DoS attacks, compared to other models such as XGBoost (98.48%) and multidimensional feature approaches (98.96%). The model’s deployment on a web-based platform demonstrated its ability to filter and classify incoming network traffic into normal or attack types like SIDDOS and UDP-Flood in real-time, effectively mitigating the threats posed by DoS attacks.

keywords:

Denial of Service (DoS), Random Forest Classifier, k-means, Cybersecurity.

---

References:

1. Sheikh, M., Liang, J., & Wang, W. (2019). A survey of security services, attacks, and
applications for vehicular ad hoc networks (vanets). Sensors, 19(16), 3589.
Htun, T. &Tun, Z., (2018). Algorithm tuning from comparative analysis of classification
algorithms. International Journal of Research and Studies Publishing, 8(5).
Smeureanu et al., 2013. Customer segmentation in private banking sector using machine
learning techniques. International journal of business economics and management, 14(5),
pp.923-939.
Cil, A.E., Yildiz, K. & Buldu, A., (2021). Detection of DDoS attacks with feed forward
based deep neural network model. Expert Systems with Applications, 169, 114520
Sandhya, N. & Charanjeet, K.R., (2016). A review of machine learning techniques.
International Journal on Recent and Innovation Trends in Computing and Communication,
4(3), 451-458.
Pedreira, V., Barros, D. & Pinto, P., (2021). A review of attacks, vulnerabilities, and
defenses in industry 4.0 with new challenges on data sovereignty ahead. Sensors, 21(15),
p.5189.
Sarker, I. (2021). Data science and analytics: an overview from data-driven smart
computing, decision-making and applications perspective. Sn Computer Science, 2(5).
Mrabet, H., Belguith, S., Alhomoud, A. & Jemai, A., (2020). A survey of IoT security
based on a layered architecture of sensing and data analysis. Sensors, 20(13), p.3625.
Bithas, P., Michailidis, E., ???????, ?., Vouyioukas, D., & Kanatas, ?. (2019). A survey
on machine-learning techniques for uav-based communications. Sensors, 19(23), 5170.
Horne, E., Tibble, H., Sheikh, A., & Tsanas, A. (2020). Challenges of clustering
multimodal clinical data: review of applications in asthma subtyping. Jmir Medical
Informatics, 8(5), e16452.
Tatam, M., Shanmugam, B., Azam, S., & Kannoorpatti, K. (2021). A review of threat
modelling approaches for apt-style attacks. Heliyon, 7(1), e05969.
Djedouboum, A., Ari, A., Gueroui, A., Mohamadou, A., & Aliouat, Z. (2018). Big data
collection in large-scale wireless sensor networks. Sensors, 18(12), 4474.
Kruse, C., Frederick, B., Jacobson, T., & Monticone, D. (2017). Cybersecurity in
healthcare: a systematic review of modern threats and trends. Technology and Health Care,
25(1), 1-10.
Pedreira, V., Barros, D., & Pinto, P. (2021). A review of attacks, vulnerabilities, and
defenses in industry 4.0 with new challenges on data sovereignty ahead. Sensors, 21(15),
Sufriyana, H., Husnayain, A., Chen, Y., Kuo, C., Singh, O., Yeh, T., … & Su, E. (2020).
Comparison of multivariable logistic regression and other machine learning algorithms for
prognostic prediction studies in pregnancy care: systematic review and meta-analysis. Jmir
Medical Informatics, 8(11), e16503.
Seo, H., Khuzani, M., Vasudevan, V., Huang, C., Ren, H., Xiao, R., … & Li, X. (2020).
Machine learning techniques for biomedical image segmentation: an overview of technical
aspects and introduction to state?of?art applications. Medical Physics, 47(5).
Sarica, A., Cerasa, A., & Quattrone, A. (2017). Random forest algorithm for the
classification of neuroimaging data in alzheimer's disease: a systematic review. Frontiers
in Aging Neuroscience, 9.

DOWNLOAD PDF

---

Back