篇名 |
Research on Industrial IoT Security Based on Deep Learning
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並列篇名 | Research on Industrial IoT Security Based on Deep Learning |
作者 | Xian Guo、Keyu Chen、An Yang、Zhanhui Gang |
英文摘要 | With the rapid development of "Internet +" and the construction of a new generation of information infrastructure, the intrusion behaviors against the Industrial Internet of Things are increasingly common. How to ensure the security of the industrial Internet of Things is one of the current research hotspots. The modern technology trend has the hottest technologies of the Internet of Things (IoT). The application of IoT on the other hand improves work efficiency and brings convenience to people’s life; on the other hand, it makes the network face increasingly serious security threat problems and attacks the network by unscrupulous elements occur from time to time. Machine learning-based intrusion detection techniques involve a large number of mathematical formula operations, while with the development of neural networks, the excellent autonomous feature learning capability of deep learning is recognized. An intrusion detection system plays an important role in preventing security threats and protecting them from attacks. The current research on industrial IoT security technology focuses on authentication technology, encryption technology, access control technology, and intrusion detection technology. In this paper, we analyze deep learning and industrial IoT intrusion detection and use the powerful data processing capability and feature learning capability of deep learning to conduct an in-depth study on industrial IoT intrusion detection methods based on deep learning. This paper achieves a 96.32% detection rate on industrial control dataset, which can better adapt to the needs of industrial IoT intrusion detection.
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起訖頁 | 727-744 |
關鍵詞 | Industrial internet of things、Intrusion detection、Deep learning、Inception-CNN、Detection methods |
刊名 | 網際網路技術學刊 |
期數 | 202305 (24:3期) |
出版單位 | 台灣學術網路管理委員會 |
DOI |
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