What question did this study set out to answer?

To develop a real-time DDoS detection and mitigation framework suitable for consumer-centric SDN applications with low latency requirements.

August 7, 2026Open Access

Adaptive hybrid framework for low-latency DDoS defense in consumer-centric SDN architectures

SBSumit BadotraNational University of Singapore STSarvesh TanwarAmity University SVSourabh Singh VermaManipal Academy of Higher Education

Key Points

To develop a real-time DDoS detection and mitigation framework suitable for consumer-centric SDN applications with low latency requirements.
Proposed an adaptive hybrid framework integrating multiple techniques like LSTM, Reinforcement Learning, Genetic Algorithm, and ARIMA.
Focused on applications with stringent low-latency needs such as telemedicine and online gaming.
Experimental evaluation conducted to measure detection accuracy and performance metrics.
Achieved an average detection accuracy of 97.08%.
Reduced latency by 80% compared to traditional methods.
Resulted in a packet loss rate as low as 0.027%.

Abstract

Abstract Software-Defined Networking (SDN) increases networking flexibility, scalability, and programmability by separating the control plane from the data plane. Nevertheless, this structure is also vulnerable to DDoS, which may result in performance degradation and service unavailability. We propose a new adaptive hybrid real-time DDoS detection and mitigation framework designed for consumer applications with tight low latency constraints such as telemedicine, online gaming, and video streaming services. The suggested architectural model incorporates four well-known techniques (LSTM-based DeepPredict-DDoS, Reinforcement Learning-based Adaptive DeepPredict-DDoS, Genetic Algorithm-based DeepPredict-GA, and ARIMA-based DeepPredict-ARIMA) through an innovative decision-making engine. Experimental results show an average detection accuracy of 97.08%, reduced latency by 80%, and packet loss rate as low as 0.027%. These properties make the solution scalable, efficient, and effective for consumer-level SDN systems. Graphic Abstract

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