International Journal of Emerging Research in Science, Engineering, and Management
Vol. 2, Issue 1, pp. 306-311, January 2026.
This work is licensed under a Creative Commons Attribution 4.0 International License.
B Jhansi
T Susmitha
Shivam Saurabh
Paipuri Vamsi Krishna
Shaik Mastan Hussain
Alisha Sinha
Department of CSE, Siddartha Institute of Science and Technology, Puttur, India.
Abstract: Distributed Denial of Service (DDoS) attacks continue to pose a significant threat to modern network infrastructures, particularly due to the emergence of previously unseen and evolving attack patterns. Conventional intrusion detection systems predominantly rely on closed-set assumptions, limiting their effectiveness against unknown or zero-day DDoS attacks. To address this challenge, this paper proposes an open-set recognition framework for DDoS attack detection based on Reciprocal Points Learning (RPL). The proposed approach leverages network traffic features such as flow duration, packet statistics, and statistical flow characteristics to distinguish normal traffic, known DDoS attacks, and unknown attack instances. Machine learning classifiers, including Passive Aggressive, Random Forest, and Decision Tree models, are employed to evaluate the effectiveness of the proposed framework. Experimental analysis demonstrates that the integration of Reciprocal Points Learning significantly enhances the system’s capability to identify unknown DDoS attacks while maintaining high detection accuracy for known classes. The results indicate that the proposed framework provides a robust and adaptable solution for proactive DDoS detection in dynamic and evolving network environments.
Keywords: Open-set recognition, DDoS attack detection, Reciprocal Points Learning, Machine learning, Network security.
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