An IoT Device Identification Method based on Semi-supervised Learning
| Autor: | Zhiliang Wang, Chenxin Duan, Linna Fan, Yichao Wu, Shize Zhang, Jia Li, Jiahai Yang |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
021110 strategic
defence & security studies Computer science Network security business.industry Supervised learning Feature extraction 0211 other engineering and technologies 020206 networking & telecommunications 02 engineering and technology Solid modeling Semi-supervised learning Machine learning computer.software_genre Identification (information) Server 0202 electrical engineering electronic engineering information engineering Artificial intelligence business computer Protocol (object-oriented programming) |
| Zdroj: | CNSM |
| DOI: | 10.23919/cnsm50824.2020.9269044 |
| Popis: | With the rapid proliferation of IoT devices, device management and network security are becoming significant challenges. Knowing how many IoT devices are in the network and whether they are behaving normally is significant. IoT device identification is the first step to achieve these goals. Previous IoT identification works mainly use supervised learning and need lots of labeled data. Considering collecting labeled data is time-consuming and cannot be scaled, in this paper, we propose an IoT identification model based on semi-supervised learning. The model can differentiate IoT and non-IoT and classify specific IoT devices based on time interval features, traffic volume features, protocol features and TLS related features. The evaluation in a public dataset shows that our model only needs 5% labeled data and gets accuracy over 99%. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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