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Since the Industrial Internet of Things (IIoT) is being used widely across many industries, protecting systems used in the IIoT has grown crucial. In order to protect internet of things networks from criminal activity, intrusion detection is essential. In this paper, we offer a structure for deep learning for IIoT intrusion detection that depends on Convolutional neural networks (CNNs). We make use of the reference data set NSL-KDD, which is frequently used to assess intrusion detection systems. The suggested architecture eliminates the requirement for human feature engineering by utilizing CNNs’ built-in capacity to obtain attributes from network traffic data. The dataset is preprocessed, augmented with new data, and divided into training and testing sets. Next, the training set is used to train the CNN model, and the testing set is used to assess it. Our CNN-based intrusion detection system outperforms conventional machine learning techniques in regards to precision, recall, precision, and F1-score, as shown by experimental findings. The suggested architecture offers an effective way to find intrusions in IIoT systems, strengthening their resilience and safety.

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Analysis on Convolutional Neural Networks for Deep Learning Intrusion Identification Using Industrial Internet of Things

  • 会议论文

  • 发表日期：

    2025年

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  • K. Srujan Raju,
  • Mohd. Abdul Naqi,
  • V. A. Narayana,
  • Vivekanand Aelgani,
  • Rajesh Tiwari,
  • Sheo Kumar

摘要

Since the Industrial Internet of Things (IIoT) is being used widely across many industries, protecting systems used in the IIoT has grown crucial. In order to protect internet of things networks from criminal activity, intrusion detection is essential. In this paper, we offer a structure for deep learning for IIoT intrusion detection that depends on Convolutional neural networks (CNNs). We make use of the reference data set NSL-KDD, which is frequently used to assess intrusion detection systems. The suggested architecture eliminates the requirement for human feature engineering by utilizing CNNs’ built-in capacity to obtain attributes from network traffic data. The dataset is preprocessed, augmented with new data, and divided into training and testing sets. Next, the training set is used to train the CNN model, and the testing set is used to assess it. Our CNN-based intrusion detection system outperforms conventional machine learning techniques in regards to precision, recall, precision, and F1-score, as shown by experimental findings. The suggested architecture offers an effective way to find intrusions in IIoT systems, strengthening their resilience and safety.