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作 者:Jinghui Zhang Xinyang Li Junhe Li Qiangsheng Dai Zhen Ling Ming Yang
机构地区:[1]School of Computer Science and Engineering,Southeast University,Nanjing 211189,China [2]School of Cyber Science and Engineering,Southeast University,Nanjing 211189,China [3]Research Institute,State Grid Jiangsu Electric Power Company Ltd.,Nanjing 210024,China
出 处:《Tsinghua Science and Technology》2023年第5期862-872,共11页清华大学学报(自然科学版(英文版)
基 金:supported by the National Natural Science Foundation of China(Nos.61972085,62072103,62232004);the Jiangsu Provincial Key R&D Program(Nos.BE2021729,BE2022680,BE2022065-4);the Jiangsu Provincial Key Laboratory of Network and Information Security(No.BM2003201);the Key Laboratory of Computer Network and Information Integration of Ministry of Education of China(No.93K-9);the Collaborative Innovation Center of Novel Software Technology and Industrialization,the Fundamental Research Funds for the Central Universities,the CCF-Baidu Open Fund(No.2021PP15002000);the Future Network Scientific Research Fund Project(No.FNSRFP-2021-YB-02).
摘 要:With the rapid development of the Internet of Things(IoT),wireless technology has become an indispensable part of modern computing platforms and embedded systems.Wireless device fingerprint identification is deemed as a promising solution towards enhancing the security of device access authentication and communication process in the IoT scenario.However,the extraction of features from the network layer and its upper layers often confront restrictions from specific devices:the association with a certain wireless network and the access to the plaintext of the payload.Meanwhile,Bluetooth Low Energy(BLE)packets have been encrypted above the link layer,which makes those features difficult to extract.To tackle these problems,we introduce a novel method to identify BLE devices based on the fingerprint features in the data link layer.Initially,the BLE packets are collected through a receiver based on software-defined radio technology.Then,fields that reflect device differences in BLE broadcast packets are extracted through traffic analysis.Finally,a MultiLayer Perceptron(MLP)model is employed to recognize the category of BLE devices.An experimental result on a dataset with 15 types of BLE devices shows that the identification accuracy of the proposed method can reach 99.8%,which accomplishes better performance over previous work.
关 键 词:link layer fingerprint software-defined radio Bluetooth Low Energy(BLE)
分 类 号:TN9[电子电信—信息与通信工程]
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