IoT-ID : Robust IoT Device Identification Based on Feature Drift Adaptation

Chen, Qi and Song, Yubo and Jennings, Brendan and Zhang, Fan and Xiao, Bin and Gao, Shang (2021) IoT-ID : Robust IoT Device Identification Based on Feature Drift Adaptation. In: 2021 IEEE Global Communications Conference, GLOBECOM 2021 - Proceedings :. 2021 IEEE Global Communications Conference, GLOBECOM 2021 - Proceedings . Institute of Electrical and Electronics Engineers Inc., ESP. ISBN 9781728181042

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Internet of Things (IoT) devices deployed in publicly accessible locations increasingly encounter security threats from device replacement and impersonation attacks. Unfortunately, the limited memory and poor computing capability on such devices make solutions involving complex algorithms or enhanced authentication protocols untenable. To address this issue, device identification technologies based on traffic characteristics finger-printing have been proposed to prevent illegal device intrusion and impersonation. However, because of time-dependent distribution of traffic characteristics, these approaches often become less accurate over time. Meanwhile insufficient attention has been paid to the impact of possible changes on the accuracy of device identification. Therefore, we propose a novel feature selection method based on degree of feature drift and genetic algorithm to keep high accuracy and stability of device identification. The degree of feature drift - relevance of features through time and gain ratio are combined as a composite metric to filter out stable features. Furthermore, in order to perform equally well in device identification, we use the genetic algorithm to select the most discriminate feature subset. Experiments show that the accuracy of device recognition compared with other methods is increased from 86.4% to 94.5%, and the robustness of recognition is also improved.

Item Type: Book Section
Additional Information: Funding Information: ACKNOWLEDGEMENTS This work is supported in part by National Key R&D Program of China and Frontiers Science Center for Mobile Information Communication and Security, under Grant Nos. 2018YFB2202200 and 2018YFB2100403, and in part by a research grant from Science Foundation Ireland (SFI) that is co-funded under the European Regional Development Fund under Grant Number 13/RC/2077. The corresponding author is Yubo Song. Publisher Copyright: © 2021 IEEE.
Uncontrolled Keywords: /dk/atira/pure/subjectarea/asjc/1700/1702
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Depositing User: Admin SSL
Date Deposited: 19 Oct 2022 23:18
Last Modified: 11 Aug 2023 08:00

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