Identification of IoT Devices Based on Hardware and Software Fingerprint Features

被引:0
|
作者
Jiang, Yu [1 ,2 ,3 ,4 ]
Dou, Yufei [1 ]
Hu, Aiqun [1 ,4 ,5 ,6 ]
机构
[1] Southeast Univ, Sch Cyber Sci & Engn, Nanjing 210000, Peoples R China
[2] Purple Mt Labs, Nanjing 210000, Peoples R China
[3] Key Lab Comp Network Technol Jiangsu Prov, Nanjing 210000, Peoples R China
[4] Southeast Univ, Frontiers Sci Ctr Mobile Informat Commun & Secur, Nanjing 210000, Peoples R China
[5] Southeast Univ, Sch Informat Sci & Engn, Nanjing 210000, Peoples R China
[6] Southeast Univ, State Key Lab Mobile Commun, Nanjing 210000, Peoples R China
来源
SYMMETRY-BASEL | 2024年 / 16卷 / 07期
基金
国家重点研发计划; 中国国家自然科学基金;
关键词
Internet of things; hardware and software fingerprint features; device identification; multimodal;
D O I
10.3390/sym16070846
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Unauthenticated device access to a network presents substantial security risks. To address the challenges of access and identification for a vast number of devices with diverse functions in the era of the Internet of things (IoT), we propose an IoT device identification method based on hardware and software fingerprint features. This approach aims to achieve comprehensive "hardware-software-user" authentication. First, by extracting multimodal hardware fingerprint elements, we achieve identity authentication at the device hardware level. The time-domain and frequency-domain features of the device's transient signals are extracted and further learned by a feature learning network to generate device-related time-domain and frequency-domain feature representations. These feature representations are fused using a splicing operation, and the fused features are input into the classifier to identify the device's hardware attribute information. Next, based on the interaction traffic, behavioral information modeling and sequence information modeling are performed to extract the behavioral fingerprint elements of the device, achieving authentication at the software level. Experimental results demonstrate that the method proposed in this paper exhibits a high detection efficacy, achieving 99% accuracy in both software and hardware level identification.
引用
收藏
页数:21
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