ImagTIDS: an internet of things intrusion detection framework utilizing GADF imaging encoding and improved Transformer
Abstract As the Internet of Things (IoT) technology becomes extensively deployed, IoT security issues are increasingly prominent. The traffic patterns of IoT are complex and high-dimensional, which makes it difficult to distinguish the tiny differences between normal and malicious samples. To tackle...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2024-12-01
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| Series: | Complex & Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40747-024-01712-9 |
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| _version_ | 1832571147892293632 |
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| author | Peng Wang Yafei Song Xiaodan Wang Xiangke Guo Qian Xiang |
| author_facet | Peng Wang Yafei Song Xiaodan Wang Xiangke Guo Qian Xiang |
| author_sort | Peng Wang |
| collection | DOAJ |
| description | Abstract As the Internet of Things (IoT) technology becomes extensively deployed, IoT security issues are increasingly prominent. The traffic patterns of IoT are complex and high-dimensional, which makes it difficult to distinguish the tiny differences between normal and malicious samples. To tackle the above problems, we propose an IoT intrusion detection architecture based on Gramian angular difference fields (GADF) imaging technology and improved Transformer, named ImagTIDS. Firstly, we encode the network traffic data of IoT into images using GADF to preserve more robust temporal and global features, and then we propose a model named ImagTrans for extracting local and global features from network traffic images. ImagTIDS utilizes the self-attention mechanism to dynamically adjust the attention weights and adaptively focus on the important features, effectively suppressing the adverse effects of redundant features. Furthermore, due to the serious class imbalance problem in IoT intrusion detection, we utilize Focal Loss to dynamically scale the model gradient and adaptively reduce the weights of simple samples to focus on hard-to-classify classes. Finally, we validate the effectiveness of the proposed method on the publicly available IoT intrusion detection datasets ToN_IoT and DS2OS, and the experimental results show that the proposed method achieves superior detection performance and higher robustness on class imbalance datasets compared to other remarkable methods. |
| format | Article |
| id | doaj-art-1b43b5853a77407ba40a480b8be9839c |
| institution | Kabale University |
| issn | 2199-4536 2198-6053 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj-art-1b43b5853a77407ba40a480b8be9839c2025-02-02T12:49:02ZengSpringerComplex & Intelligent Systems2199-45362198-60532024-12-0111111610.1007/s40747-024-01712-9ImagTIDS: an internet of things intrusion detection framework utilizing GADF imaging encoding and improved TransformerPeng Wang0Yafei Song1Xiaodan Wang2Xiangke Guo3Qian Xiang4College of Air and Missile Defense, Air Force Engineering UniversityCollege of Air and Missile Defense, Air Force Engineering UniversityCollege of Air and Missile Defense, Air Force Engineering UniversityCollege of Air and Missile Defense, Air Force Engineering UniversityCollege of Air and Missile Defense, Air Force Engineering UniversityAbstract As the Internet of Things (IoT) technology becomes extensively deployed, IoT security issues are increasingly prominent. The traffic patterns of IoT are complex and high-dimensional, which makes it difficult to distinguish the tiny differences between normal and malicious samples. To tackle the above problems, we propose an IoT intrusion detection architecture based on Gramian angular difference fields (GADF) imaging technology and improved Transformer, named ImagTIDS. Firstly, we encode the network traffic data of IoT into images using GADF to preserve more robust temporal and global features, and then we propose a model named ImagTrans for extracting local and global features from network traffic images. ImagTIDS utilizes the self-attention mechanism to dynamically adjust the attention weights and adaptively focus on the important features, effectively suppressing the adverse effects of redundant features. Furthermore, due to the serious class imbalance problem in IoT intrusion detection, we utilize Focal Loss to dynamically scale the model gradient and adaptively reduce the weights of simple samples to focus on hard-to-classify classes. Finally, we validate the effectiveness of the proposed method on the publicly available IoT intrusion detection datasets ToN_IoT and DS2OS, and the experimental results show that the proposed method achieves superior detection performance and higher robustness on class imbalance datasets compared to other remarkable methods.https://doi.org/10.1007/s40747-024-01712-9Internet of ThingsIntrusion detectionVision transformerGramian angular difference fieldsCyber security |
| spellingShingle | Peng Wang Yafei Song Xiaodan Wang Xiangke Guo Qian Xiang ImagTIDS: an internet of things intrusion detection framework utilizing GADF imaging encoding and improved Transformer Complex & Intelligent Systems Internet of Things Intrusion detection Vision transformer Gramian angular difference fields Cyber security |
| title | ImagTIDS: an internet of things intrusion detection framework utilizing GADF imaging encoding and improved Transformer |
| title_full | ImagTIDS: an internet of things intrusion detection framework utilizing GADF imaging encoding and improved Transformer |
| title_fullStr | ImagTIDS: an internet of things intrusion detection framework utilizing GADF imaging encoding and improved Transformer |
| title_full_unstemmed | ImagTIDS: an internet of things intrusion detection framework utilizing GADF imaging encoding and improved Transformer |
| title_short | ImagTIDS: an internet of things intrusion detection framework utilizing GADF imaging encoding and improved Transformer |
| title_sort | imagtids an internet of things intrusion detection framework utilizing gadf imaging encoding and improved transformer |
| topic | Internet of Things Intrusion detection Vision transformer Gramian angular difference fields Cyber security |
| url | https://doi.org/10.1007/s40747-024-01712-9 |
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