Two-stage multi-classification algorithm for Internet of Things equipment identification

SONG Yubo; QI Xinyu; HUANG Qiang; HU Aiqun; YANG Junjie

doi:10.16511/j.cnki.qhdxxb.2019.22.050

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Journal of Tsinghua University(Science and Technology) ›› 2020, Vol. 60 ›› Issue (5) : 365-370. DOI: 10.16511/j.cnki.qhdxxb.2019.22.050

SPECIAL SECTION:VULNERABILITY ANALYSIS AND RISK ASSESSMENT

Two-stage multi-classification algorithm for Internet of Things equipment identification

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Abstract

The Internet of Things will have a large number of devices interconnected through the network with effective network access control needed to avoid damage from malicious devices on the system. At present, the most effective method is to extract network traffic characteristics as the device fingerprint for device identification since this method requires relatively few network resources. However, existing device identification algorithms are not very accurate, especially for similar devices since classification overlap is unavoidable. This paper presents a two-stage multi-classification algorithm that identifies the equipment according to its network traffic characteristics. When classification overlap occurs, the maximum similarity comparison algorithm is used for secondary classification. Tests show that the average recognition accuracy of this algorithm is 93.2%.

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device identification / multi-classification technology / maximum similarity / machine learning

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SONG Yubo, QI Xinyu, HUANG Qiang, HU Aiqun, YANG Junjie. Two-stage multi-classification algorithm for Internet of Things equipment identification[J]. Journal of Tsinghua University(Science and Technology). 2020, 60(5): 365-370 https://doi.org/10.16511/j.cnki.qhdxxb.2019.22.050

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