BLS-identification:A device fingerprint classification mechanism based on broad learning for Internet of Things

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外文摘要：The popularity of the Internet of Things(IoT)has enabled a large number of vulnerable devices to connect to the Internet,bringing huge security risks.As a network-level security authentication method,device fingerprint based on machine learning has attracted considerable attention because it can detect vulnerable devices in complex and heterogeneous access phases.However,flexible and diversified IoT devices with limited resources increase dif-ficulty of the device fingerprint authentication method executed in IoT,because it needs to retrain the model network to deal with incremental features or types.To address this problem,a device fingerprinting mechanism based on a Broad Learning System(BLS)is proposed in this paper.The mechanism firstly characterizes IoT devices by traffic analysis based on the identifiable differences of the traffic data of IoT devices,and extracts feature parameters of the traffic packets.A hierarchical hybrid sampling method is designed at the preprocessing phase to improve the imbalanced data distribution and reconstruct the fingerprint dataset.The complexity of the dataset is reduced using Principal Component Analysis(PCA)and the device type is identified by training weights using BLS.The experimental results show that the proposed method can achieve state-of-the-art accuracy and spend less training time than other existing methods.

外文关键词：

Device fingerprintTraffic analysisClass imbalanceBroad learning systemAccess authentication

作者：

Yu Zhang、Bei Gong、Qian Wang

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作者单位：

Faculty of Information Technology,Beijing University of Technology,Beijing,100124,China

School of Information Science and Technology,Zhengzhou Normal University,Henan,450044,China

基金：

National Key R&D Program of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaYoung Backbone Teacher Training Program of Henan Colleges and Universities

项目编号：

2019YFB2102303NSFC61971014NSFC116751992021GGJS170

出版年：

2024

DOI：

10.1016/j.dcan.2022.10.003

数字通信与网络(英文)

ISSN：

年,卷(期)：2024.10(3)