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<p>The rapid adoption of Software-Defined Networking (SDN) in cloud, IoT, and Big Data environments introduces significant security challenges, particularly for the SDN controller, which lacks built-in protection and remains vulnerable to Denial-of-Service (DDoS) attacks. This study proposes an enhanced deep learning-based detection mechanism for identifying multi-rate DDoS attacks in an SDN controller. The proposed mechanism is evaluated under two attack scenarios and achieves an average accuracy of 99.91% for high-rate and 99.21% for low-rate attacks when detecting DDoS flooding traffic. The results demonstrate the effectiveness and robustness of the proposed mechanism in protecting the SDN controller against DDoS attacks.</p>

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Deep Learning-Based Detection Mechanism for DDoS Attacks Targeting SDN Controller

  • 期刊论文

  • 发表日期：

    2026年4月1日

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  • Abdullah Ahmed Bahashwan,
  • Mohammed Anbar,
  • Selvakumar Manickam,
  • Ali Abdulqader Bin-Salem

摘要

The rapid adoption of Software-Defined Networking (SDN) in cloud, IoT, and Big Data environments introduces significant security challenges, particularly for the SDN controller, which lacks built-in protection and remains vulnerable to Denial-of-Service (DDoS) attacks. This study proposes an enhanced deep learning-based detection mechanism for identifying multi-rate DDoS attacks in an SDN controller. The proposed mechanism is evaluated under two attack scenarios and achieves an average accuracy of 99.91% for high-rate and 99.21% for low-rate attacks when detecting DDoS flooding traffic. The results demonstrate the effectiveness and robustness of the proposed mechanism in protecting the SDN controller against DDoS attacks.