Scholarlink

<p>The advent of Software-Defined Networking (SDN) has transformed the landscape of cloud network management by decoupling the control and data planes, enabling centralized programmability and dynamic policy enforcement. However, this paradigm shift introduces new challenges for real-time anomaly detection and DDoS mitigation. This paper proposes an intelligent anomaly mitigation framework that leverages deep learning techniques—specifically Sparse Autoencoders (SAE) for feature extraction and a hybrid CNN-LSTM model for capturing both spatial and temporal traffic anomalies. The system is implemented and evaluated using Python 3.9 with TensorFlow 2.x on a Ryu SDN controller integrated with OpenFlow-based switches in Mininet. Experiments are conducted across various SDN topologies including Star, Mesh, Tree, and Random configurations. Benchmark datasets CIC-DDoS2019 and UNSW-NB15 are used for model training and validation. The proposed framework achieves 99.1% detection accuracy, a 30% improvement in false positive rate over traditional methods, and reduces mitigation time by up to 40%, confirming its suitability for real-time deployment in dynamic SDN environments.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

SAE-CNN-LSTM-based anomaly detection and mitigation framework for cloud-centric SDN environments

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月25日

查看全文
  • Ankush Mehra,
  • Gurpreet Singh,
  • Ahmad Almogren,
  • Sumit Badotra,
  • Vishnu Kant,
  • Seada Hussen,
  • Ateeq Ur Rehman

摘要

The advent of Software-Defined Networking (SDN) has transformed the landscape of cloud network management by decoupling the control and data planes, enabling centralized programmability and dynamic policy enforcement. However, this paradigm shift introduces new challenges for real-time anomaly detection and DDoS mitigation. This paper proposes an intelligent anomaly mitigation framework that leverages deep learning techniques—specifically Sparse Autoencoders (SAE) for feature extraction and a hybrid CNN-LSTM model for capturing both spatial and temporal traffic anomalies. The system is implemented and evaluated using Python 3.9 with TensorFlow 2.x on a Ryu SDN controller integrated with OpenFlow-based switches in Mininet. Experiments are conducted across various SDN topologies including Star, Mesh, Tree, and Random configurations. Benchmark datasets CIC-DDoS2019 and UNSW-NB15 are used for model training and validation. The proposed framework achieves 99.1% detection accuracy, a 30% improvement in false positive rate over traditional methods, and reduces mitigation time by up to 40%, confirming its suitability for real-time deployment in dynamic SDN environments.