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<p>Software-Defined Networking (SDN) offers centralized control and programmability, transforming network management but also introducing vulnerabilities, particularly to Distributed Denial of Service (DDoS) attacks that can overwhelm the control plane and disrupt network functionality. Traditional DDoS detection methods, including rule-based systems and conventional machine learning models, often fall short in SDN due to high false-positive rates and limited adaptability to evolving network traffic. While recent deep learning approaches show promise, they continue to face challenges with real-time adaptability and scalability in SDN environments. In this study, we propose Attention-Enhanced Cross-Entropy (AECE), a novel Deep Neural Network (DNN)-based DDoS detection model that integrates attention mechanisms to prioritize critical features in network traffic data, allowing the model to focus on patterns indicative of DDoS attacks. A core innovation in AECE is the F-Balanced Cross-Entropy (FBCE) Loss function, which combines cross-entropy with an F1-score-based component to balance precision and recall, effectively reducing both false positives and false negatives. Additionally, AECE incorporates ReLU and GELU activations, batch normalization, dropout, and the Adamax optimizer to enhance learning stability and computational efficiency. Experimental results demonstrate that the proposed system achieves high detection accuracy, significantly outperforming existing DDoS detection methods and providing a robust, low-latency solution to safeguard SDN infrastructures against evolving DDoS threats.</p>

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Enhancing SDN security with deep learning and F-balanced cross-entropy for DDoS detection

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月29日

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  • Hossein Monshizadeh Naeen,
  • Malihe Ghadamyari,
  • Mobin Barmar

摘要

Software-Defined Networking (SDN) offers centralized control and programmability, transforming network management but also introducing vulnerabilities, particularly to Distributed Denial of Service (DDoS) attacks that can overwhelm the control plane and disrupt network functionality. Traditional DDoS detection methods, including rule-based systems and conventional machine learning models, often fall short in SDN due to high false-positive rates and limited adaptability to evolving network traffic. While recent deep learning approaches show promise, they continue to face challenges with real-time adaptability and scalability in SDN environments. In this study, we propose Attention-Enhanced Cross-Entropy (AECE), a novel Deep Neural Network (DNN)-based DDoS detection model that integrates attention mechanisms to prioritize critical features in network traffic data, allowing the model to focus on patterns indicative of DDoS attacks. A core innovation in AECE is the F-Balanced Cross-Entropy (FBCE) Loss function, which combines cross-entropy with an F1-score-based component to balance precision and recall, effectively reducing both false positives and false negatives. Additionally, AECE incorporates ReLU and GELU activations, batch normalization, dropout, and the Adamax optimizer to enhance learning stability and computational efficiency. Experimental results demonstrate that the proposed system achieves high detection accuracy, significantly outperforming existing DDoS detection methods and providing a robust, low-latency solution to safeguard SDN infrastructures against evolving DDoS threats.