TCN enhanced novel malicious traffic detection for IoT devices
With the development of IoT technology, more and more IoT devices are connected to the network. Due to the hardware constraints of IoT devices themselves, it is difficult for developers to embed security software into them. Therefore, it is better to protect IoT devices at the traffic level. The eff...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2022-12-01
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| Series: | Connection Science |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/09540091.2022.2067124 |
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| Summary: | With the development of IoT technology, more and more IoT devices are connected to the network. Due to the hardware constraints of IoT devices themselves, it is difficult for developers to embed security software into them. Therefore, it is better to protect IoT devices at the traffic level. The effect of malicious traffic detection based on neural networks is promising. Still, the slow computation brings some difficulties to deploying AI-based detection systems on edge servers. Time Convolutional Network (TCN) is a high-speed neural network suitable for massively parallel computation. In this paper, we propose Multi-class S-TCN, an improved network supporting multiple classifications based on TCN for the practical needs of IoT scenarios. Besides, we implement a complete IoT traffic security detection procedure based on deep packet inspection and protocol analysis. The proposed Multi-class S-TCN significantly improves the detection speed without degrading the detection effect. Experiments show that this work has better detection performance and faster detection speed compared to existing approaches, proving the effectiveness of the proposed detection flow and Multi-class S-TCN in IoT scenarios. |
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| ISSN: | 0954-0091 1360-0494 |