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<p>Amplified Reflection Distributed Denial of Service (AR-DDoS) attack has become a serious attack on the SDN controller, and the control channel bandwidth degrades network performance. In an AR-DDoS attack, attackers exploit vulnerable reflectors and amplify traffic towards the victim, overwhelming network resources. This saturates network bandwidth, the centralized controller, and the flow tables, and increases queuing delay. Existing solutions detect AR-DDoS attacks using sketches and traffic statistics experience suboptimal detection accuracy due to inadequately designed algorithms. Moreover, previous solutions demonstrate machine learning accuracy in classifying attack traffic; they face difficulty in detecting emerging AR-DDoS attacks. The ever-evolving attack landscape, containing new features, necessitates continuous model retraining. In this paper, we propose a resource-efficient Quantized Convolutional Neural Network (QCNN) model deployed within the programmable data plane, designed for intelligent traffic monitoring and accurate detection of AR-DDoS attacks. Specifically, quantization of a Convolution Neural Network model is performed to easily infer the model in a resource-constrained programmable data plane. Further, this model is mapped to the match and action tables to efficiently detect and mitigate attack traffic. Furthermore, a self-learning mechanism is used to improve detection in a dynamic network environment. In addition, in-network traffic prioritization has been performed with PQ-Sketch to prevent queue congestion and drop attack flows. Extensive experiments on the Tofino hardware testbed show that QCNN achieves 99.83, 98.66, and 98.95% accuracy in classifying attack traffic using CIC-IDS 2017, CICDDoS 2019 datasets, and generated traces, outperforming existing methods.</p>

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Intelligent and Resource-Efficient Framework for AR-DDoS Attack Detection and Mitigation in Programmable Data Plane Using QCNN

  • 期刊论文

  • 发表日期：

    2026年5月8日

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  • Lilima Jain,
  • Venkanna U.,
  • Satyanarayana Vollala

摘要

Amplified Reflection Distributed Denial of Service (AR-DDoS) attack has become a serious attack on the SDN controller, and the control channel bandwidth degrades network performance. In an AR-DDoS attack, attackers exploit vulnerable reflectors and amplify traffic towards the victim, overwhelming network resources. This saturates network bandwidth, the centralized controller, and the flow tables, and increases queuing delay. Existing solutions detect AR-DDoS attacks using sketches and traffic statistics experience suboptimal detection accuracy due to inadequately designed algorithms. Moreover, previous solutions demonstrate machine learning accuracy in classifying attack traffic; they face difficulty in detecting emerging AR-DDoS attacks. The ever-evolving attack landscape, containing new features, necessitates continuous model retraining. In this paper, we propose a resource-efficient Quantized Convolutional Neural Network (QCNN) model deployed within the programmable data plane, designed for intelligent traffic monitoring and accurate detection of AR-DDoS attacks. Specifically, quantization of a Convolution Neural Network model is performed to easily infer the model in a resource-constrained programmable data plane. Further, this model is mapped to the match and action tables to efficiently detect and mitigate attack traffic. Furthermore, a self-learning mechanism is used to improve detection in a dynamic network environment. In addition, in-network traffic prioritization has been performed with PQ-Sketch to prevent queue congestion and drop attack flows. Extensive experiments on the Tofino hardware testbed show that QCNN achieves 99.83, 98.66, and 98.95% accuracy in classifying attack traffic using CIC-IDS 2017, CICDDoS 2019 datasets, and generated traces, outperforming existing methods.