Vol. 7 No. 2 (2025), Articles
Vol. 7 No. 2 (2025)

Detection of DDOS Attacks using Ensemble and Probabilistic Classifiers

Articles
Published 27-05-2025
Pushpavalli K.
Assistant Professor, Department of Information Technology, Jerusalem College of Engineering, Chennai, India
Meena R.
Department of Information Technology, Jerusalem College of Engineering, Chennai, India
Ranganayaki E.
Department of Information Technology, Jerusalem College of Engineering, Chennai, India
PDF

Keywords

DDoS Attacks
Cybersecurity
Machine Learning
Traffic Analysis
Detection Accuracy
Performance Analysis
Real-time Detection

How to Cite

Detection of DDOS Attacks using Ensemble and Probabilistic Classifiers. (2025). Journal of Information Technology and Digital World, 7(2), 106-118. https://doi.org/10.36548/jitdw.2025.2.002
ACM ACS APA ABNT Chicago Harvard IEEE MLA Turabian Vancouver AMA

Download Citation

Endnote/Zotero/Mendeley (RIS) BibTeX

Abstract

Distributed Denial of Service (DDoS) attacks disrupt online services, leading to operational and financial losses. This project presents a machine learning-based detection system utilizing Logistic Regression, Decision Tree, Random Forest, and Gaussian Naive Bayes to analyze network traffic and identify anomalies. By employing data preprocessing and extracting critical features, the system enhances accuracy and efficiency in distinguishing between normal and malicious traffic. Each algorithm is evaluated based on performance, scalability, and computational efficiency. Future enhancements will focus on real-time monitoring and the exploration of more advanced models to improve detection capabilities.

PDF

References

Satyanarayana, D., and Aisha Said Alasmi. "Detection and mitigation of DDOS based attacks using machine learning algorithm." In 2022 International Conference on Cyber Resilience (ICCR), pp. IEEE, (2022) 1-5.

Ajeetha, G., and G. Madhu Priya. "Machine learning based DDOS attack detection." In 2019 Innovations in Power and Advanced Computing Technologies (i-PACT), vol. 1, pp. IEEE, (2019) 1-5.

Saini, Parvinder Singh, Sunny Behal, and Sajal Bhatia. "Detection of DDoS attacks using machine learning algorithms." In 2020 7th International Conference on Computing for Sustainable Global Development (INDIACom), pp. IEEE, (2020) 16-21.

Santhosh, S., M. Sambath, and J. Thangakumar. "Detection of DDOS attack using machine learning models." In 2023 International Conference on Networking and Communications (ICNWC), pp. IEEE, (2023) 1-6.

Kumar, P. Arun Raj, and S. Selvakumar. "Distributed denial of service attack detection using an ensemble of neural classifier." Computer Communications 34, no. 11 (2011): 1328-1341.

Katkar, Vijay D., and Siddhant Vijay Kulkarni. "Experiments on detection of Denial of Service attacks using ensemble of classifiers." In 2013 International Conference on Green Computing, Communication and Conservation of Energy (ICGCE), pp. IEEE, (2013) 837-842.

Alguliyev, R. M., R. M. Aliguliyev, Y. N. Imamverdiyev, and L. V. Sukhostat. "An improved ensemble approach for DoS attacks detection." Радіоелектроніка, інформатика, управління 2 (45) (2018): 73-82.

Kumar, P. Arun Raj, and S. Selvakumar. "Detection of distributed denial of service attacks using an ensemble of adaptive and hybrid neuro-fuzzy systems." Computer Communications 36, no. 3 (2013): 303-319.

Das, Saikat, Deepak Venugopal, and Sajjan Shiva. "A holistic approach for detecting DDoS attacks by using ensemble unsupervised machine learning." In Advances in Information and Communication: Proceedings of the 2020 Future of Information and Communication Conference (FICC), Volume 2, pp. 721-738. Springer International Publishing, 2020.

Hossain, Md Alamgir. "Enhanced ensemble-based distributed denial-of-service (DDoS) attack detection with novel feature selection: a robust cybersecurity approach." Artificial Intelligence Evolution (2023): 165-186