Industrial Internet of Things Cyber Threats Detection Through Deep Feature Learning and Stacked Sparse Autoencoder Based Classification
ABSTRACT In recent times, the industrial system has integrated with industrial Internet of Things (IoT) applications to enable the ease of production process and ensure worker safety. Security is a major concern in the industrial Internet of Things (IIoT) environment owing to the distributed nature...
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| Vydáno v: | Transactions on emerging telecommunications technologies Ročník 36; číslo 9 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Chichester, UK
John Wiley & Sons, Ltd
01.09.2025
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| ISSN: | 2161-3915, 2161-3915 |
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| Abstract | ABSTRACT
In recent times, the industrial system has integrated with industrial Internet of Things (IoT) applications to enable the ease of production process and ensure worker safety. Security is a major concern in the industrial Internet of Things (IIoT) environment owing to the distributed nature of architecture and dynamic traffic flows. Generally, the cyber‐attack detection model is classified as misuse and anomaly detection. The misuse detection method is employed based on the concept of signature matching, and the anomaly method is based on the detection of known and unknown attacks. Present security models have realized the issue of over‐fitting, low classification accuracy, and a high false positive rate when given a massive volume of network traffic data. The proposed work focused on “IIoT cyber‐attack detection using lightweight hybrid deep learning algorithm” to identify intrusion. At first, the data imbalance problem is resolved through the Euclidean‐based synthetic minority oversampling technique (EbSmoT) to prevent the model from becoming biased toward one class. Then, the Information Gain and Fisher score‐based technique (IG‐FST) is employed to eliminate redundant features and avoid overfitting problems during training. Moreover, the Bi‐LSTM ResNet‐based convolutional autoencoder (BR‐CAE) is executed to obtain higher‐level feature representation. Finally, a Stacked Sparse autoencoder‐based Particle Swarm Probabilistic Neural Network (SAE‐PSPNN) is used for attack detection and classification. The performance of the proposed method can be evaluated using several performance metrics through two different datasets, such as the UNSW‐NB15 dataset and the ToN_IoT dataset. The proposed framework achieved an accuracy of 99.86% on the ToN_IoT dataset and 99.62% on the UNSW‐NB15 dataset.
To resolve the data imbalance issues, the Euclidean‐based synthetic minority oversampling technique (EbSmoT) is utilized in the preprocessing stage.
To eliminate the redundant features, the Information Gain and Fisher score based technique (IG‐FST) neglects the overfitting issues while training.
To obtain the higher level feature representation, a Bi‐LSTM ResNet based convolutional auto encoder (BR‐CAE) network is used.
To detect and classify the anomaly in IIoT, the stacked parse Autoencoder Particle Swarm Probabilistic Neural Network (SAE‐PSPNN) is used. |
|---|---|
| AbstractList | ABSTRACT
In recent times, the industrial system has integrated with industrial Internet of Things (IoT) applications to enable the ease of production process and ensure worker safety. Security is a major concern in the industrial Internet of Things (IIoT) environment owing to the distributed nature of architecture and dynamic traffic flows. Generally, the cyber‐attack detection model is classified as misuse and anomaly detection. The misuse detection method is employed based on the concept of signature matching, and the anomaly method is based on the detection of known and unknown attacks. Present security models have realized the issue of over‐fitting, low classification accuracy, and a high false positive rate when given a massive volume of network traffic data. The proposed work focused on “IIoT cyber‐attack detection using lightweight hybrid deep learning algorithm” to identify intrusion. At first, the data imbalance problem is resolved through the Euclidean‐based synthetic minority oversampling technique (EbSmoT) to prevent the model from becoming biased toward one class. Then, the Information Gain and Fisher score‐based technique (IG‐FST) is employed to eliminate redundant features and avoid overfitting problems during training. Moreover, the Bi‐LSTM ResNet‐based convolutional autoencoder (BR‐CAE) is executed to obtain higher‐level feature representation. Finally, a Stacked Sparse autoencoder‐based Particle Swarm Probabilistic Neural Network (SAE‐PSPNN) is used for attack detection and classification. The performance of the proposed method can be evaluated using several performance metrics through two different datasets, such as the UNSW‐NB15 dataset and the ToN_IoT dataset. The proposed framework achieved an accuracy of 99.86% on the ToN_IoT dataset and 99.62% on the UNSW‐NB15 dataset.
To resolve the data imbalance issues, the Euclidean‐based synthetic minority oversampling technique (EbSmoT) is utilized in the preprocessing stage.
To eliminate the redundant features, the Information Gain and Fisher score based technique (IG‐FST) neglects the overfitting issues while training.
To obtain the higher level feature representation, a Bi‐LSTM ResNet based convolutional auto encoder (BR‐CAE) network is used.
To detect and classify the anomaly in IIoT, the stacked parse Autoencoder Particle Swarm Probabilistic Neural Network (SAE‐PSPNN) is used. In recent times, the industrial system has integrated with industrial Internet of Things (IoT) applications to enable the ease of production process and ensure worker safety. Security is a major concern in the industrial Internet of Things (IIoT) environment owing to the distributed nature of architecture and dynamic traffic flows. Generally, the cyber‐attack detection model is classified as misuse and anomaly detection. The misuse detection method is employed based on the concept of signature matching, and the anomaly method is based on the detection of known and unknown attacks. Present security models have realized the issue of over‐fitting, low classification accuracy, and a high false positive rate when given a massive volume of network traffic data. The proposed work focused on “IIoT cyber‐attack detection using lightweight hybrid deep learning algorithm” to identify intrusion. At first, the data imbalance problem is resolved through the Euclidean‐based synthetic minority oversampling technique (EbSmoT) to prevent the model from becoming biased toward one class. Then, the Information Gain and Fisher score‐based technique (IG‐FST) is employed to eliminate redundant features and avoid overfitting problems during training. Moreover, the Bi‐LSTM ResNet‐based convolutional autoencoder (BR‐CAE) is executed to obtain higher‐level feature representation. Finally, a Stacked Sparse autoencoder‐based Particle Swarm Probabilistic Neural Network (SAE‐PSPNN) is used for attack detection and classification. The performance of the proposed method can be evaluated using several performance metrics through two different datasets, such as the UNSW‐NB15 dataset and the ToN_IoT dataset. The proposed framework achieved an accuracy of 99.86% on the ToN_IoT dataset and 99.62% on the UNSW‐NB15 dataset. |
| Author | Kesavan, M. Senthil Kumar, C. Abdullah, Azween Bin Vijay Anand, R. Brahmam, Madala Guru Magesh, G. Alagiri, I. |
| Author_xml | – sequence: 1 givenname: R. surname: Vijay Anand fullname: Vijay Anand, R. email: vijayanand.r@vit.ac.in organization: Vellore Institute of Technology – sequence: 2 givenname: G. surname: Magesh fullname: Magesh, G. organization: Vellore Institute of Technology – sequence: 3 givenname: I. surname: Alagiri fullname: Alagiri, I. organization: Vellore Institute of Technology – sequence: 4 givenname: Madala Guru surname: Brahmam fullname: Brahmam, Madala Guru organization: VIT‐AP University, Amaravati – sequence: 5 givenname: C. surname: Senthil Kumar fullname: Senthil Kumar, C. organization: Vellore Institute of Technology – sequence: 6 givenname: M. surname: Kesavan fullname: Kesavan, M. organization: Comcast – sequence: 7 givenname: Azween Bin surname: Abdullah fullname: Abdullah, Azween Bin organization: Pedana University |
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In recent times, the industrial system has integrated with industrial Internet of Things (IoT) applications to enable the ease of production process... In recent times, the industrial system has integrated with industrial Internet of Things (IoT) applications to enable the ease of production process and ensure... |
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| SubjectTerms | anomaly detection bi‐directional LSTM deep learning industrial internet of things particle swarm optimization probabilistic neural network sparse autoencoder |
| Title | Industrial Internet of Things Cyber Threats Detection Through Deep Feature Learning and Stacked Sparse Autoencoder Based Classification |
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