计算机科学 ›› 2021, Vol. 48 ›› Issue (9): 286-291.doi: 10.11896/jsjkx.201100130

• 计算机网络 • 上一篇    下一篇

无线帧间隔特征提取方法

李双秋1, 余志斌1, 杨玲2, 张译方2, 刘莉萍1   

  1. 1 西南交通大学电气工程学院 成都611756
    2 中国电子科技集团公司第二十九研究所 成都610036
  • 收稿日期:2020-11-17 修回日期:2021-03-15 出版日期:2021-09-15 发布日期:2021-09-10
  • 通讯作者: 余志斌(zbyu@swjtu.edu.cn.)
  • 作者简介:838933378@qq.com
  • 基金资助:
    装备发展部领域基金(61403120304);电磁空间应用重点实验室基金

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

摘要: 针对现有无线网络设备个体识别方法精度不高、高采样率以及需解析协议等问题,文中从无线帧行为角度出发,研究并提出了无线帧间隔特征提取算法。该方法基于无线帧间隔特征生成机理,利用不同型号设备的无线帧间的间隔差异,研究面向单目标无线设备和多目标无线设备的信标帧帧间隔特征提取算法,并以无线路由器为例验证了该方法的有效性。实验结果表明,在同型号和不同型号无线设备混合且每次单个设备开启的情况下,所提方法对设备个体的平均识别率达到了94%,比传统方法提高了近10%;当多个无线设备同时开启时,所提方法对设备个体的识别率也达到了90%。从理论分析和实验验证结果可知,信标帧间隔作为识别无线路由设备的指纹特征,能够有效区分不同型号的无线路由设备。所提方法无需高精度采样即可获取瞬态信号,不易受调制方式的影响,也无需解析协议,非常适合通信对抗和网络安全中无线网络设备的个体识别。

关键词: 个体识别, 网络设备, 无线帧, 帧行为

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

中图分类号: 

  • 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] 宋国正,吴亚锋.
基于SNMP及构件组装技术的设备仿真模型
Equipment Simulation Model Based on SNMP and Component Assembly Technology
计算机科学, 2012, 39(Z6): 187-189.
[2] 李红.
FACT协议研究

计算机科学, 2004, 31(12): 19-22.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!