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<p>The rapid expansion of the Internet of Things (IoT) has significantly improved the various aspects of our daily life. However, along with its benefits, new security threats such as Denial of Service (DoS) attacks and Botnets have emerged. To adopt this technology and integrity of IoT environment, detection of such attacks become crucial. This paper proposes a hybrid deep learning model that combines Convolutional Neural Network (CNN) and Gated Recurrent Units (GRUs) to classify the IoT security threats. The CNN is used to extract the spatial features from the network data, where on the other hand GRUs used for capturing the temporal dependencies. This combination makes the model effective at analysing both static and dynamic aspects of network data. Further, to optimize the performance of the proposed hybrid model, self-upgraded Cat and Mouse Optimization (SUCMO) algorithm is employed, a state of art optimization technique. The SUCMO algorithm fine-tunes the deep learning model’s hyperparameters to improve classification accuracy. The proposed model is evaluated through experiments on two different datasets i.e., UNSW-NB15 and BoT-IoT, and results demonstrates that proposed work outperforms the traditional work as well as state of the art works.</p>

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Advances to IoT security using a GRU-CNN deep learning model trained on SUCMO algorithm

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月12日

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  • Amit Sagu,
  • Nasib Singh Gill,
  • Preeti Gulia,
  • Noha Alduaiji,
  • Piyush Kumar Shukla,
  • Mohd Asif Shah

摘要

The rapid expansion of the Internet of Things (IoT) has significantly improved the various aspects of our daily life. However, along with its benefits, new security threats such as Denial of Service (DoS) attacks and Botnets have emerged. To adopt this technology and integrity of IoT environment, detection of such attacks become crucial. This paper proposes a hybrid deep learning model that combines Convolutional Neural Network (CNN) and Gated Recurrent Units (GRUs) to classify the IoT security threats. The CNN is used to extract the spatial features from the network data, where on the other hand GRUs used for capturing the temporal dependencies. This combination makes the model effective at analysing both static and dynamic aspects of network data. Further, to optimize the performance of the proposed hybrid model, self-upgraded Cat and Mouse Optimization (SUCMO) algorithm is employed, a state of art optimization technique. The SUCMO algorithm fine-tunes the deep learning model’s hyperparameters to improve classification accuracy. The proposed model is evaluated through experiments on two different datasets i.e., UNSW-NB15 and BoT-IoT, and results demonstrates that proposed work outperforms the traditional work as well as state of the art works.