CTSF: An Intrusion Detection Framework for Industrial Internet Based on Enhanced Feature Extraction and Decision Optimization Approach
The traditional Transformer model primarily employs a self-attention mechanism to capture global feature relationships, potentially overlooking local relationships within sequences and thus affecting the modeling capability of local features. For Support Vector Machine (SVM), it often requires the j...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/21/8793 |
| _version_ | 1797631272067006464 |
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| author | Guangzhao Chai Shiming Li Yu Yang Guohui Zhou Yuhe Wang |
| author_facet | Guangzhao Chai Shiming Li Yu Yang Guohui Zhou Yuhe Wang |
| author_sort | Guangzhao Chai |
| collection | DOAJ |
| description | The traditional Transformer model primarily employs a self-attention mechanism to capture global feature relationships, potentially overlooking local relationships within sequences and thus affecting the modeling capability of local features. For Support Vector Machine (SVM), it often requires the joint use of feature selection algorithms or model optimization methods to achieve maximum classification accuracy. Addressing the issues in both models, this paper introduces a novel network framework, CTSF, specifically designed for Industrial Internet intrusion detection. CTSF effectively addresses the limitations of traditional Transformers in extracting local features while compensating for the weaknesses of SVM. The framework comprises a pre-training component and a decision-making component. The pre-training section consists of both CNN and an enhanced Transformer, designed to capture both local and global features from input data while reducing data feature dimensions. The improved Transformer simultaneously decreases certain training parameters within CTSF, making it more suitable for the Industrial Internet environment. The classification section is composed of SVM, which receives initial classification data from the pre-training phase and determines the optimal decision boundary. The proposed framework is evaluated on an imbalanced subset of the X-IIOTID dataset, which represent Industrial Internet data. Experimental results demonstrate that with SVM using both “linear” and “rbf” kernel functions, CTSF achieves an overall accuracy of 0.98875 and effectively discriminates minor classes, showcasing the superiority of this framework. |
| first_indexed | 2024-03-11T11:21:29Z |
| format | Article |
| id | doaj.art-84931e94dc974538a88dfa84a20b9078 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T11:21:29Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-84931e94dc974538a88dfa84a20b90782023-11-10T15:12:06ZengMDPI AGSensors1424-82202023-10-012321879310.3390/s23218793CTSF: An Intrusion Detection Framework for Industrial Internet Based on Enhanced Feature Extraction and Decision Optimization ApproachGuangzhao Chai0Shiming Li1Yu Yang2Guohui Zhou3Yuhe Wang4College of Computer Science and Information Engineering, Harbin Normal University, Harbin 150025, ChinaCollege of Computer Science and Information Engineering, Harbin Normal University, Harbin 150025, ChinaCollege of Computer Science and Information Engineering, Harbin Normal University, Harbin 150025, ChinaCollege of Computer Science and Information Engineering, Harbin Normal University, Harbin 150025, ChinaCollege of Computer Science and Information Engineering, Harbin Normal University, Harbin 150025, ChinaThe traditional Transformer model primarily employs a self-attention mechanism to capture global feature relationships, potentially overlooking local relationships within sequences and thus affecting the modeling capability of local features. For Support Vector Machine (SVM), it often requires the joint use of feature selection algorithms or model optimization methods to achieve maximum classification accuracy. Addressing the issues in both models, this paper introduces a novel network framework, CTSF, specifically designed for Industrial Internet intrusion detection. CTSF effectively addresses the limitations of traditional Transformers in extracting local features while compensating for the weaknesses of SVM. The framework comprises a pre-training component and a decision-making component. The pre-training section consists of both CNN and an enhanced Transformer, designed to capture both local and global features from input data while reducing data feature dimensions. The improved Transformer simultaneously decreases certain training parameters within CTSF, making it more suitable for the Industrial Internet environment. The classification section is composed of SVM, which receives initial classification data from the pre-training phase and determines the optimal decision boundary. The proposed framework is evaluated on an imbalanced subset of the X-IIOTID dataset, which represent Industrial Internet data. Experimental results demonstrate that with SVM using both “linear” and “rbf” kernel functions, CTSF achieves an overall accuracy of 0.98875 and effectively discriminates minor classes, showcasing the superiority of this framework.https://www.mdpi.com/1424-8220/23/21/8793Industrial Internetintrusion detectionconvolutional neural networkTransformerSupport Vector Machines |
| spellingShingle | Guangzhao Chai Shiming Li Yu Yang Guohui Zhou Yuhe Wang CTSF: An Intrusion Detection Framework for Industrial Internet Based on Enhanced Feature Extraction and Decision Optimization Approach Sensors Industrial Internet intrusion detection convolutional neural network Transformer Support Vector Machines |
| title | CTSF: An Intrusion Detection Framework for Industrial Internet Based on Enhanced Feature Extraction and Decision Optimization Approach |
| title_full | CTSF: An Intrusion Detection Framework for Industrial Internet Based on Enhanced Feature Extraction and Decision Optimization Approach |
| title_fullStr | CTSF: An Intrusion Detection Framework for Industrial Internet Based on Enhanced Feature Extraction and Decision Optimization Approach |
| title_full_unstemmed | CTSF: An Intrusion Detection Framework for Industrial Internet Based on Enhanced Feature Extraction and Decision Optimization Approach |
| title_short | CTSF: An Intrusion Detection Framework for Industrial Internet Based on Enhanced Feature Extraction and Decision Optimization Approach |
| title_sort | ctsf an intrusion detection framework for industrial internet based on enhanced feature extraction and decision optimization approach |
| topic | Industrial Internet intrusion detection convolutional neural network Transformer Support Vector Machines |
| url | https://www.mdpi.com/1424-8220/23/21/8793 |
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