Published on: April 2026
A FRAMEWORK FOR MACHINE LEARNING-BASED INTRUSION DETECTION TO FIND DENIAL OF SERVICE ATTACKS IN NETWORK TRAFFIC
Bollepalle Jhansi
K Naresh
Article Status
Available Documents
Abstract
In contemporary network systems, denial of service (DoS) assaults are among the most prevalent and disruptive cyberthreats. By overloading network resources, these attacks seek to prevent genuine users from accessing services. Because of the growing complexity and volume of network traffic, traditional intrusion detection systems frequently fail to detect such attacks. This paper suggests a machine learning-based intrusion detection system intended to successfully identify DoS assaults as a solution to this problem. The suggested approach trains a machine learning model that can differentiate between benign and malicious traffic patterns using network traffic data. To enhance model performance, data preparation methods including feature scaling and feature selection are used.
How to Cite this Paper
Jhansi, B. (2026). A Framework for Machine Learning-Based Intrusion Detection to Find Denial of Service Attacks in Network Traffic. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(04). https://doi.org/10.55041/ijcope.v2i4.059
Jhansi, Bollepalle. "A Framework for Machine Learning-Based Intrusion Detection to Find Denial of Service Attacks in Network Traffic." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i4.059.
Jhansi, Bollepalle. "A Framework for Machine Learning-Based Intrusion Detection to Find Denial of Service Attacks in Network Traffic." International Journal of Creative and Open Research in Engineering and Management 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i4.059.
Search & Index
References
[1] C. F. Tsai, Y. F. Hsu, C. Y. Lin, and W. Y. Lin, “Intrusion Detection by Machine Learning: A Review,” Expert Systems with Applications, vol. 36, no. 10, pp. 11994–12000, 2009.[2] R. Sommer and V. Paxson, “Outside the Closed World: On Using Machine Learning for Network Intrusion Detection,” in Proceedings of the IEEE Symposium on Security and Privacy, 2010, pp. 305–316.
[3] A. L. Buczak and E. Guven, “A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection,” IEEE Communications Surveys & Tutorials, vol. 18, no. 2, pp. 1153–1176, 2016.
[4] G. Kim, S. Lee, and S. Kim, “A Novel Hybrid Intrusion Detection Method Integrating Anomaly Detection with Misuse Detection,” Expert Systems with Applications, vol. 41, no. 4, pp. 1690–1700, 2014.
[5] V. Chandola, A. Banerjee, and V. Kumar, “Anomaly Detection: A Survey,” ACM Computing Surveys, vol. 41, no. 3, pp. 1–58, 2009
Ethical Compliance & Review Process
- •All submissions are screened under plagiarism detection.
- •Review follows editorial policy.
- •Authors retain copyright.
- •Peer Review Type: Double-Blind Peer Review
- •Published on: Apr 06 2026
This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. You are free to share and adapt this work for non-commercial purposes with proper attribution.