基于超高频RFID的被动生物识别电子锁

doi:10.11959/j.issn.2096-109x.2021022

网络与信息安全学报 ›› 2021, Vol. 7 ›› Issue (2): 126-140.doi: 10.11959/j.issn.2096-109x.2021022

基于超高频RFID的被动生物识别电子锁

邹祥¹^,², 韩劲松², 曲宇航¹, 肖剑³, 许贤⁴

¹ 西安交通大学计算机科学与技术学院，陕西西安 710049
² 浙江大学网络空间安全学院，浙江杭州 310007
³ 长安大学电子与控制工程学院，陕西西安 710064
⁴ 浙江大学建筑工程学院，浙江杭州 310085

修回日期:2020-09-23 出版日期:2021-04-15 发布日期:2021-04-01
作者简介:邹祥（1992- ），男，陕西西安人，西安交通大学博士生，主要研究方向为移动计算、物联网安全。
韩劲松（1975- ），男，山东泰安人，浙江大学教授、博士生导师，主要研究方向为物联网安全。
曲宇航（1996- ），男，陕西西安人，西安交通大学硕士生，主要研究方向为移动计算、RFID。
肖剑（1975- ），男，甘肃庆阳人，长安大学副教授，主要研究方向为智能感知与系统。
许贤（1981- ），男，浙江杭州人，浙江大学教授、博士生导师，主要研究方向为建筑结构智能监测。
基金资助:
国家自然科学基金(61872285);国家自然科学基金(61772413);浙江大学网络空间国际治理研究基地，浙江省引进培育领军型创新创业团队(2018R01005);浙江省重点研发计划(2019C03133);浙江省重点研发计划(2017YFC0806100);国家社科基金重大项目(20ZDA062);西安市科技计划项目(201805045YD23CG29(1))

Passive biometric electronic lock via UHF RFID

Xiang ZOU¹^,², Jinsong HAN², Yuhang QU¹, Jian XIAO³, Xian XU⁴

¹ School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China
² School of Cyber Science and Technology, Zhejiang University, Hangzhou 310007, China
³ School of Electronics and Control Engineering, Chang'an University , Xi'an 710064, China
⁴ College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310085, China

Revised:2020-09-23 Online:2021-04-15 Published:2021-04-01
Supported by:
The National Natural Science Foundation of China(61872285);The National Natural Science Foundation of China(61772413);The Research Institute of Cyberspace Governance in Zhejiang University, Leading Innovative and Entrepreneur Team Introduction Program of Zhejiang(2018R01005);The Key R＆D Program of Zhejiang Province, China(2019C03133);The Key R＆D Program of Zhejiang Province, China(2017YFC0806100);Major Projects of the National Social Science Foundation(20ZDA062);Xi'an Science and Technology Plan Project(201805045YD23CG29(1))

摘要/Abstract

摘要：

提出了一种利用人体内部的生物特征来进行认证的生物识别电子锁设计方法，简称为 PBLock。该方法通过人手指与无源式射频标签的接触，利用后向散射的射频信号传递人体指尖阻抗特征来实行认证。这样的认证方式的优势在于，由于人体阻抗不易采集，加之与设备（RFID 标签）的硬件特征充分融合，大大增加了攻击者复制和克隆的难度。为保证系统的可用性和高效性，利用标签天线剪切式认证优化机制，有效提升了人体阻抗敏感度。同时，利用RFID系统的电磁能量优势讨论了被动驱动的可行性。实验评估结果表明，PBLock的认证准确率可达 96%，平均单次认证的时间花销是1.4 s。另外，从实际环境出发提出了攻击模型，通过深入分析发现，PBLock 可以有效防止伪造攻击、模仿攻击和重放攻击，为新型生物识别电子锁的应用安全提供重要保证。

关键词: 生物识别电子锁, 阻抗生物特征, 射频识别, 近场耦合

Abstract:

A biometric lock design method was proposed based on the internal features of human body, namely PBLock.It used the backscattered RF signals to collect the impedance characteristics of human fingertips through the contact between human fingers and tags.The advantage of such authentication method is that the human impedance was not easy to be stolen, and it was fully integrated with the hardware characteristics of the device (RFID tags), which greatly increased the difficulty for the attacker to copy and clone.To ensure the availability and efficiency of the system, an optimization authentication mechanism was proposed by cutting tag antenna, which effectively improved the impedance sensitivity.Moreover, the feasibility of passive drive was discussed by using the electromagnetic energy advantage of RFID system.Through a large number of experimental evaluations, the authentication accuracy of PBLock can reach 96%, and the average time cost of a single authentication is 1.4 seconds.Some attack models were presented based on practical environment.The results show that PBLock can effectively prevent counterfeiting attack, impersonation attack and replay attack, which provides an opportunity for the secure application of new biometric electronic locks.

Key words: biometric lock, impedance biometric, radio frequency identification, near field coupling

中图分类号:

TP309

邹祥, 韩劲松, 曲宇航, 肖剑, 许贤. 基于超高频RFID的被动生物识别电子锁[J]. 网络与信息安全学报, 2021, 7(2): 126-140.

Xiang ZOU, Jinsong HAN, Yuhang QU, Jian XIAO, Xian XU. Passive biometric electronic lock via UHF RFID[J]. Chinese Journal of Network and Information Security, 2021, 7(2): 126-140.

图/表 15

图1

图2

图3

图4

图5

图6

图7

图8

图9

图10

图11

图12

图13

图14

图15

参考文献 27

[1]	CAO K , JAIN A K . Hacking mobile phones using 2D printed fingerprints[R]. Michigan State University Tech.MSU-CSE-16-2, 2016.
[2]	MOSHNYAGA V G , SHIOYAMA J , HASHIMOTO K . A camera-based approach to prevent fingerprint hacking[C]// IEEE International Workshop on Signal Processing Systems. 2018.
[3]	BOWYER K W , DOYLE J S . Cosmetic contact lenses and IRIS recognition spoofing[J]. Computer, 2014,47(5): 96-98.
[4]	ERDOGMUS N , MARCEL S . Spoofing in 2D face recognition with 3D masks and anti-spoofing with Kinect[C]// Proc IEEE 6th Int Conf Biometrics Theory Appl Syst. 2013.
[5]	RUIZ-ALBACETE V , TOME-GONZALEZ P , ALONSO-FERNANDEZ F , ,et al. Direct attacks using fake images in IRIS verification[C]// Proc 1st Eur Workshop Biometrics Identity Manage. 2008.
[6]	KIBRET B , TESHOME A K , LAI D T ,et al. Analysis of the human body as an antenna for wireless implant communication[J]. IEEE Transactions on Antennas and Propagation, 2016,64(4): 1466-1476.
[7]	ARAI N , SASAKI K , MURAMATSU D ,et al. Variation of impedance in transmission channel of human body communication[C]// International Symposium on Medical Information and Communication Technology. 2017: 49-53.
[8]	SHAHRIAR H , HADDAD H M , ISLAM M ,et al. An iris-based authentication framework to prevent presentation attacks[C]// Computer Software and Applications Conference. 2017: 504-509.
[9]	MIRMOGAMADSADEGHI L , DRYGAJLO A . Palm vein recognition with local binary patterns and local derivative patterns[C]// International Joint Conference on Biometrics. 2011.
[10]	MIURA N , NAGASAKA A , MIYATAKE T . Feature extraction of finger-vein patterns based on repeated line tracking and its application to personal identification[J]. Systems ＆ Computers in Japan, 2004,35(7): 61-71.
[11]	ARORA P , SRIVASTAVA S , HANMANDLU M ,et al. Robust authentication using dorsal hand vein images[J]. IEEE Intelligent Systems, 2019,34(2): 25-35.
[12]	BOLES A , RAD P . Voice biometrics:deep learning-based voiceprint authentication system[C]// Service Oriented Software Engineering. 2017: 1-6.
[13]	REN Y , FANG Z , LIU D ,et al. Replay attack detection based on distortion by loudspeaker for voice authentication[J]. Multimedia Tools and Applications, 2019,78(7): 8383-8396.
[14]	YANG Y F , GUO B , WANG Z ,et al. BehaveSense:continuous authentication for security-sensitive mobile Apps using behavioral biometrics[J]. Ad Hoc Networks, 2019,84: 9-18.
[15]	MUSALE P , BAEK D , WERELLAGAMA N ,et al. You walk,we authenticate:lightweight seamless authentication based on gait in wearable IoT systems[J]. IEEE Access, 2019: 37883-37895.
[16]	WANG Y X , CHEN Y N , BHUIYAN M Z A ,et al. Gait-based human identification using acoustic sensor and deep neural network[J]. Future Generation Computer Systems, 2018,86: 1228-1237.
[17]	ZENG Y , PATHAK P H , MOHAPATRA P ,et al. WiWho:wifi-based person identification in smart spaces[C]// Information Processing in Sensor networks,IPSN. 2016.
[18]	ZHANG J , WEI B , HU W ,et al. WiFi-ID:human identification using Wi-Fi signal[C]// Distributed Computing in Sensor systems,DCOSS. 2016.
[19]	KONG H , LU L , YU J ,et al. Finger pass:finger gesture-based continuous user authentication for smart homes using commodity Wi-Fi[C]// International Symposium on Mobile Ad Hoc Networking and Computing,MobiHoc. 2019.
[20]	LIN F , SONG C , ZHUANG Y ,et al. Cardiac scan:A non-contact and continuous heart-based user authentication system[C]// IEEE International Conference on Mobile Computing and Networking. 2017.
[21]	CHAUHAN J , SENEVIRATNE S , HU Y ,et al. Breathing-based authentication on resource-constrained IoT devices using recurrent neural networks[J]. IEEE Computer, 2018,51(5): 60-67.
[22]	ZHANG L , TAN S , YANG J . Hearing your voice is not enough:an articulatory gesture based liveness detection for voice authentication[C]// ACM Conference on Computer and Communications Security,CCS. 2017.
[23]	OH S , YANG J , BIANCHI A ,et al. Devil in a box:installing backdoors in electronic door locks[C]// Conference on Privacy Security and Trust, 2015: 139-144.
[24]	ZHAO C , LI Z , LIU T ,et al. RF-Mehndi:a fingertip profiled RF identifier[C]// International Conference on Computer Communications,INFOCOM. 2019.
[25]	HAN J S , QIAN C , WANG X ,et al. Twins:device-free object tracking using passive tags[C]// International Conference on Computer Communications,INFOCOM. 2014.
[26]	DING H , HAN J S , QIAN C ,et al. Trio:utilizing tag interference for refined localization of passive RFID[C]// International Conference on Computer Communications,INFOCOM. 2018.
[27]	LI P , AN Z L , YANG L ,et al. Towards physical-layer vibration sensing with RFIDs[C]// IEEE Conference on Computer Communications,INFOCOM. 2019.

基于超高频RFID的被动生物识别电子锁

Passive biometric electronic lock via UHF RFID

在线阅读

pdf下载

可视化

摘要/Abstract

引用本文

使用本文

图/表 15

参考文献 27

相关文章 15

Metrics

推荐阅读 0

[1]	王艺龙, 李震宇, 巩道福, 刘粉林. 基于块邻域的图像双脆弱水印算法[J]. 网络与信息安全学报, 2023, 9(3): 38-48.
[2]	陈任峰, 朱鸿斌. 基于PU learning的信用卡交易安全监管研究[J]. 网络与信息安全学报, 2023, 9(3): 73-78.
[3]	冯冠云, 付才, 吕建强, 韩兰胜. 基于操作注意力和数据增强的内部威胁检测[J]. 网络与信息安全学报, 2023, 9(3): 102-112.
[4]	谢根琳, 程国振, 王亚文, 王庆丰. 基于gadget特征分析的软件多样性评估方法[J]. 网络与信息安全学报, 2023, 9(3): 161-173.
[5]	侯鹏, 李智鑫, 张飞, 孙旭, 陈丹, 崔毅浩, 张寒冰, 荆一楠, 柴洪峰. 金融数据安全治理智能化技术与实践[J]. 网络与信息安全学报, 2023, 9(3): 174-187.
[6]	肖敏, 毛发英, 黄永洪, 曹云飞. 基于属性签名的车载网匿名信任管理方案[J]. 网络与信息安全学报, 2023, 9(2): 33-45.
[7]	许建龙, 林健, 黎宇森, 熊智. 分布式用户隐私保护可调节的云服务个性化QoS预测模型[J]. 网络与信息安全学报, 2023, 9(2): 70-80.
[8]	陈训逊, 李明哲, 吕宁, 黄亮. 内禀安全：网络安全能力体系化构建方法[J]. 网络与信息安全学报, 2023, 9(1): 92-102.
[9]	宋佳烁, 李祯祯, 丁海洋, 李子臣. 椭圆曲线上高效可完全模拟的不经意传输协议[J]. 网络与信息安全学报, 2023, 9(1): 158-166.
[10]	李凤华, 李晖, 牛犇, 邱卫东. 隐私计算的学术内涵与研究趋势[J]. 网络与信息安全学报, 2022, 8(6): 1-8.
[11]	唐飞, 甘宁, 阳祥贵, 王金洋. 基于区块链与国密SM9的抗恶意KGC无证书签名方案[J]. 网络与信息安全学报, 2022, 8(6): 9-19.
[12]	白雪, 秦宝东, 郭瑞, 郑东. 基于SM2的两方协作盲签名协议[J]. 网络与信息安全学报, 2022, 8(6): 39-51.
[13]	刘军, 袁霖, 冯志尚. 集群网络密钥管理方案研究综述[J]. 网络与信息安全学报, 2022, 8(6): 52-69.
[14]	肖敏, 姚涛, 刘媛妮, 黄永洪. 具有隐私保护的动态高效车载云管理方案[J]. 网络与信息安全学报, 2022, 8(6): 70-83.
[15]	林佳滢, 周文柏, 张卫明, 俞能海. 空域频域相结合的唇型篡改检测方法[J]. 网络与信息安全学报, 2022, 8(6): 146-155.