Computer Science ›› 2021, Vol. 48 ›› Issue (9): 286-291.doi: 10.11896/jsjkx.201100130

• Computer Network • Previous Articles     Next Articles

Extraction Method of Wireless Frame Interval Feature

LI Shuang-qiu1, YU Zhi-bin1, YANG Ling2, ZHANG Yi-fang2, LIU Li-ping1   

  1. 1 School of Electrical Engineering Southwest Jiaotong University,Chengdu 611756,China
    2 The 29th Research Institute of CETC,Chengdu 610036,China
  • Received:2020-11-17 Revised:2021-03-15 Online:2021-09-15 Published:2021-09-10
  • About author:LI Shuang-qiu,born in 1995,postgra-duate.His main research interests include pattern recognition and so on.
    YU Zhi-bin,born in 1976,Ph.D,asso-ciate professor,Ph.D supervisor.His main research interests include signal processing,data analysis and electronic countermeasures.
  • Supported by:
    Equipment Development Department Funds(61403120304)and Key Lab of Electromagnetic Application Fund

Abstract: Aiming at the drawbacks that traditional individual identification algorithms for wireless network device are low accuracy,time-consuming and analyzing protocol,in this paper,the wireless frame interval feature extraction algorithm from the perspective of wireless frame behavior is proposed.Based on the generation mechanism of frame interval feature,the frame interval feature extraction algorithms for single-target wireless devices and multi-target wireless devices are studied,and the effectiveness of the algorithms are verified by taking the wireless router as an example.The experimental results show that when only a single device is turned on at a time experimental platform which is composed of the same model and different type wireless devices,the average recognition rate of the proposed method is 94%,which is nearly 10% higher than that of the traditional methods.When multiple wireless devices are turned on at the same time,the recognition rate of the method in this paper also reaches 90%.From theoretical analysis and experimental verification results,the frame interval of the beacon can be used to identify the wireless routing devices and distinguish different types of wireless routing devices effectively.The proposed method does not require high-precision sampling to obtain transient signals,is not susceptible to modulation mode,and does not require analyzing protocol,so it is very suitable for individual identification of wireless network equipment in communication countermeasures and network security.

Key words: Frame behavior, Individual identification, Network device, Wireless feature

CLC Number: 

  • TN971
[1]YANG N.Research on authentication technology based on radio frequency fingerprint[D].Xi'an:Xidian University,2018.
[2]ZHANG C.Progress in Time Synchronization Technology for Wireless Senso rNetworks[J].Journal of Chongqing Technology and Business University (Natural Science Edition),2019,36(6):88-94.
[3]TEKBAS O H,SERINKEN N,URETEN O.An experimental performance evaluation of a novel radio-transmitter identification system under diverse environmental conditions[J].Cana-dian Journal of Electrical and Computer Engineering,2004,29(3):203-209.
[4]SHI Z Y,LIU M,HUANG L F.Transient-based identification of 802.11b wireless device[C]//2011 International Conference on Wireless Communications and Signal Processing (WCSP).2011:1-5.
[5]LIANG J H,HUANG G Q,WANG F H,et al.Research situation and directions of transmitter individual identification technique[J].Electronic Warfare,2014,1:42-46.
[6]YU J B,HU A Q,ZHU C M.Fingerprinting extraction andidentification of wireless communication devices[J].Journal of Cryptologic Research,2016,3(5):433-446.
[7]KOSE M,TASCIOGLU S,TELATAR Z.RF fingerprinting of IoT devices based on transient energy spectrum[J].IEEE Access,2019,7:18715-18726.
[8]BALDINI G,GIULIANI R,STERI G.Physical layer authentication and identification of wireless devices using the synchrosqueezing transform[J].Applied Sciences,2018,8(2167):1-19.
[9]ZHANG J,WANG F,DOBRE O A.Specific emitter identification via Hilbert-Huang transform in single-hop and relaying scenarios[J].IEEE Transactions on Information Forensics & Security,2016,11(6):1192-1205.
[10]HAN J,ZHANG T,WANG H,et al.Communication emitter individual identification based on 3D-Hibert energy spectrum and multi-scale fractal features[J].Journal on Communications,2017(4):99-109.
[11]UDIT S,NIKITA T,GAGARIN B.Specific emitter identification based on variational mode decomposition and spectral features in single hop and relaying scenarios[J].IEEE Transactions on Information Forensics and Security,2019,14(3):581-591.
[12]PENG L,HU A,ZHANG J.Design of a hybrid RF fingerprint extraction and device classification scheme[J].IEEE Internet of Things Journal,2019,6(1):349-360.
[13]TIAN Q,JIA J,HOU C.Research on fingerprint identification of wireless devices based on information fusion[J].Mobile Networks & Applications,2020,25(6):2359-2366.
[14]CHEN Y F,HU W T,ALAM M,et al.Intelligent fingerprint learning for attacker identification in the industrial internet of things[J].IEEE Transactions on Industrial Informatics,2021,17(2):882-890.
[15]SIEKA B.Active fingerprinting of 802.11 devices by timinganalysis[C]//Consumer Communications and Networking Conference.2006:15-19.
[16]NEUMANN C.An Empirical Study of Passive 802.11 DeviceFingerprinting [C]//Distributed Computing Systems Workshops.2012:593-602.
[17]LIANG J,HAN J S,XIONG G.A passive fingerprint feature for the recognition of Cisco routers[C]//2017 IEEE 2nd Advanced Information Technology,Electronic and Automation Control Conference (IAEAC).2017:1021-1025.
[18]PENG L N,HU A Q,ZHU C M.Radio fingerprint extraction based on constellation trace figure[J].Journal of Cyber Security,2016,1(1):50-58.
[19]URETEN O,SERINKEN N.Bayesian detection of Wi-Fi transmitter RF fingerprints[J].Electronics Letters,2005,41(6):373-374.
[20]YUAN Y J,WANG X,HUANG Z T.Detection of radio tran-sient signal based on permutation entropy and GLRT[J].Wireless Personal Communications,2015,82(2):1047-1057.
[21]ZHOU C Y.Signal fingerprint identification of wireless communication equipment based on transient analysis[D].Mianyang:Southwest University of Science and Technology,2018.
[1] JIAO Xiang, WEI Xiang-lin, XUE Yu, WANG Chao, DUAN Qiang. Automatic Modulation Recognition Based on Deep Learning [J]. Computer Science, 2022, 49(5): 266-278.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!