An Easily Scalable Docker-Based Privacy-Preserving Malicious Traffic Detection Architecture for IoT Environments
As the Internet of Things (IoT) continues to evolve, its inherent diversity and rapid iteration pose challenges for the development and deployment of malicious traffic detection systems. This study aims to develop a scalable detection architecture that can accurately identify malicious traffic in Io...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10719983/ |
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| author | Tong Niu Yaqiu Liu Qingfeng Li Qichi Bao |
| author_facet | Tong Niu Yaqiu Liu Qingfeng Li Qichi Bao |
| author_sort | Tong Niu |
| collection | DOAJ |
| description | As the Internet of Things (IoT) continues to evolve, its inherent diversity and rapid iteration pose challenges for the development and deployment of malicious traffic detection systems. This study aims to develop a scalable detection architecture that can accurately identify malicious traffic in IoT environments, while protecting user privacy. We propose a deep-learning-based model that is capable of training and updating within an IoT setting. The payload of the data packets is encoded to enhance the feature extraction capability of the model. The model is then trained using federated learning/edge computing to ensure data privacy. The final model parameters are stored in an IPFS file storage system with the corresponding hash value stored in the blockchain, ensuring the correctness of the model parameters during expansion. Experiments were conducted in a Docker environment using the CIC IoT Dataset 2022 and CIC IoT Dataset 2023. The results demonstrate that the proposed architecture achieves a pre-training accuracy of 98.6% for known abnormal traffic and 79% for unknown malicious traffic. After a few rounds of training, the accuracies improved to 99.5% and 89.4%. The model also exhibited robust performance during expansion. |
| format | Article |
| id | doaj-art-bfd1ee32f76d49218ec6bd866e709289 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-bfd1ee32f76d49218ec6bd866e7092892025-08-20T02:40:13ZengIEEEIEEE Access2169-35362024-01-011219101019101910.1109/ACCESS.2024.348149610719983An Easily Scalable Docker-Based Privacy-Preserving Malicious Traffic Detection Architecture for IoT EnvironmentsTong Niu0https://orcid.org/0009-0001-9180-2214Yaqiu Liu1https://orcid.org/0000-0003-3639-9503Qingfeng Li2https://orcid.org/0000-0002-1419-8543Qichi Bao3https://orcid.org/0009-0002-2299-1936State College of Computer and Control Engineering, Northeast Forestry University, Harbin, ChinaState College of Computer and Control Engineering, Northeast Forestry University, Harbin, ChinaNetwork Information Center, Northeast Forestry University, Harbin, ChinaState College of Computer and Control Engineering, Northeast Forestry University, Harbin, ChinaAs the Internet of Things (IoT) continues to evolve, its inherent diversity and rapid iteration pose challenges for the development and deployment of malicious traffic detection systems. This study aims to develop a scalable detection architecture that can accurately identify malicious traffic in IoT environments, while protecting user privacy. We propose a deep-learning-based model that is capable of training and updating within an IoT setting. The payload of the data packets is encoded to enhance the feature extraction capability of the model. The model is then trained using federated learning/edge computing to ensure data privacy. The final model parameters are stored in an IPFS file storage system with the corresponding hash value stored in the blockchain, ensuring the correctness of the model parameters during expansion. Experiments were conducted in a Docker environment using the CIC IoT Dataset 2022 and CIC IoT Dataset 2023. The results demonstrate that the proposed architecture achieves a pre-training accuracy of 98.6% for known abnormal traffic and 79% for unknown malicious traffic. After a few rounds of training, the accuracies improved to 99.5% and 89.4%. The model also exhibited robust performance during expansion.https://ieeexplore.ieee.org/document/10719983/BlockchainInternet of Thingsmalicious traffic detectionDocker |
| spellingShingle | Tong Niu Yaqiu Liu Qingfeng Li Qichi Bao An Easily Scalable Docker-Based Privacy-Preserving Malicious Traffic Detection Architecture for IoT Environments IEEE Access Blockchain Internet of Things malicious traffic detection Docker |
| title | An Easily Scalable Docker-Based Privacy-Preserving Malicious Traffic Detection Architecture for IoT Environments |
| title_full | An Easily Scalable Docker-Based Privacy-Preserving Malicious Traffic Detection Architecture for IoT Environments |
| title_fullStr | An Easily Scalable Docker-Based Privacy-Preserving Malicious Traffic Detection Architecture for IoT Environments |
| title_full_unstemmed | An Easily Scalable Docker-Based Privacy-Preserving Malicious Traffic Detection Architecture for IoT Environments |
| title_short | An Easily Scalable Docker-Based Privacy-Preserving Malicious Traffic Detection Architecture for IoT Environments |
| title_sort | easily scalable docker based privacy preserving malicious traffic detection architecture for iot environments |
| topic | Blockchain Internet of Things malicious traffic detection Docker |
| url | https://ieeexplore.ieee.org/document/10719983/ |
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