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This work developed new SDN software in IoT environments for applying deep learning techniques over multiple classifiers: GRU, DNN, LSTM, CNN, RNN, and Ryu Controller. An NSL-KDD dataset was used to process the complex, high-dimensional data effectively to recognize the unknown intrusions which the traditional systems might miss out on. The performance of the model was experimented with parameters like accuracy, precision, recall, F-Score, and a confusion matrix. The system was developed using Python 3.11 and proved very useful; however, the model performance varies with the type of input data and the complexity of the problem it is going to solve. This paper proposes the development of SDN-based deep learning for NIDS and in collaboration with the Software Define Network and Internet of things in raising the bar of intrusion detection and underlies continuous monitoring for its efficacy.

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Developing Deep Learning-Based Network Intrusion Detection Systems (NIDS) for Iot Networks

  • 章节

  • 发表日期：

    2025年

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  • Zainab Alwaeli,
  • Olusolade Aribake Fadare,
  • Fadi Al-Turjman

摘要

This work developed new SDN software in IoT environments for applying deep learning techniques over multiple classifiers: GRU, DNN, LSTM, CNN, RNN, and Ryu Controller. An NSL-KDD dataset was used to process the complex, high-dimensional data effectively to recognize the unknown intrusions which the traditional systems might miss out on. The performance of the model was experimented with parameters like accuracy, precision, recall, F-Score, and a confusion matrix. The system was developed using Python 3.11 and proved very useful; however, the model performance varies with the type of input data and the complexity of the problem it is going to solve. This paper proposes the development of SDN-based deep learning for NIDS and in collaboration with the Software Define Network and Internet of things in raising the bar of intrusion detection and underlies continuous monitoring for its efficacy.