Spoofing Attack Detection Scheme Based on Channel Fingerprint for Millimeter Wave MIMO System

YANG Lijun; LI Minghang; LU Haitao; GUO Lin

doi:10.11999/JEIT220934

Volume 45 Issue 12

Dec. 2023

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YANG Lijun, LI Minghang, LU Haitao, GUO Lin. Spoofing Attack Detection Scheme Based on Channel Fingerprint for Millimeter Wave MIMO System[J]. Journal of Electronics & Information Technology, 2023, 45(12): 4228-4234. doi: 10.11999/JEIT220934

Citation:

YANG Lijun, LI Minghang, LU Haitao, GUO Lin. Spoofing Attack Detection Scheme Based on Channel Fingerprint for Millimeter Wave MIMO System[J]. Journal of Electronics & Information Technology, 2023, 45(12): 4228-4234. doi: 10.11999/JEIT220934

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Spoofing Attack Detection Scheme Based on Channel Fingerprint for Millimeter Wave MIMO System

doi: 10.11999/JEIT220934

YANG Lijun¹,
LI Minghang¹,
LU Haitao^{2, 3},
GUO Lin^{4
,
,}

1.
School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
2.
ZTE Corporation, Shenzhen 518057, China
3.
Shenzhen Key Laboratory of 5G RAN Security Technology Research and Application, Shenzhen 518055, China
4.
School of Modern Posts, Nanjing University of Posts and Telecommunications, Nanjing 210003, China

Funds: ZTE Industry-University-Research Fund (2023ZTE08-02), The Natural Science Foundation of Nanjing University of Posts and Telecommunications (NY222132), The National Post-doctoral Fundation (2017M621798), The Universities Natural Science Research Project of Jiangsu Province (19KJB510048), The Postgraduate Research & Practice Innovation Program of Jiangsu Province (SJCX21_0300)

Received Date: 2022-07-08
Rev Recd Date: 2023-03-30

Available Online: 2023-03-31

Publish Date: 2023-12-26

Abstract

Abstract

Millimeter wave Multiple Input and Multiple Output (MIMO) channel exhibits beam sparsity and high directivity in the beam domain, and the beam domain channel pattern is highly correlated with the terminal position. In this paper, the beam domain channel pattern is regarded as a channel fingerprint. A channel fingerprint-based identity spoofing attacks detection scheme is proposed for millimeter-wave MIMO systems. The identity authentication problem is modeled as a binary classification problem of the corresponding channel fingerprint. Then, the supervised learning Support Vector Machine (SVM) algorithm is employed to solve the classification problem. In order to achieve good classification effect, different similarity indexes on channel fingerprint are compared based on the numerical analysis of the beam domain, and the one with the best classification effect is selected as the final classification feature to train the classifier model. The simulation results show that the proposed scheme has good authentication performance even under low signal-to-noise ratio conditions. Compared with the existing relative schemes, the detection accuracy is significantly improved.
- Channel fingerprint,
- Identify spoofing Attacks,
- Millimeter-wave,
- Multiple Input and Multiple Output (MIMO),
- Support Vector Machine (SVM)

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References(22)

References

[1]	SEKER C, GÜNESER M T, and OZTURK T. A review of millimeter wave communication for 5G[C]. The 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), Ankara, Turkey, 2018: 1–5.
[2]	HEATH R W, GONZÁLEZ-PRELCIC N, RANGAN S, et al. An overview of signal processing techniques for millimeter wave MIMO systems[J]. IEEE Journal of Selected Topics in Signal Processing, 2016, 10(3): 436–453. doi: 10.1109/JSTSP.2016.2523924
[3]	WANG Ning, LI Weiwei, WANG Pu, et al. Physical layer authentication for 5G communications: Opportunities and road ahead[J]. IEEE Network, 2020, 34(6): 198–204. doi: 10.1109/MNET.011.2000122
[4]	YILMAZ M H and ARSLAN H. A survey: Spoofing attacks in physical layer security[C]. The IEEE 40th Local Computer Networks Conference Workshops (LCN Workshops), Clearwater Beach, USA, 2015: 812–817.
[5]	MENEZES A J, VAN OORSCHOT P C, and VANSTONE S A. Handbook of Applied Cryptography[M]. Boca Raton: CRC Press, 1996.
[6]	PAN Fei, WEN Hong, LIAO Runfa, et al. Physical layer authentication based on channel information and machine learning[C]. The 2017 IEEE Conference on Communications and Network Security (CNS), Las Vegas, USA, 2017: 364–365.
[7]	AHMADPOUR D and KABIRI P. Detecting forged management frames with spoofed addresses in IEEE 802.11 networks using received signal strength indicator[J]. Iran Journal of Computer Science, 2020, 3(3): 137–143. doi: 10.1007/s42044-020-00053-3
[8]	GALTIER F, CAYRE R, AURIOL G, et al. A PSD-based fingerprinting approach to detect IoT device spoofing[C]. The IEEE 25th Pacific Rim International Symposium on Dependable Computing (PRDC), Perth, Australia, 2020: 40–49.
[9]	ALAM J and KENNY P. Spoofing detection employing infinite impulse response—constant Q transform-based feature representations[C]. The 25th European Signal Processing Conference (EUSIPCO), Kos, Greece, 2017: 101–105.
[10]	SAYEED A M. Deconstructing multiantenna fading channels[J]. IEEE Transactions on Signal Processing, 2002, 50(10): 2563–2579. doi: 10.1109/TSP.2002.803324
[11]	TANG Jie, XU Aidong, JIANG Yixin, et al. MmWave MIMO physical layer authentication by using channel sparsity[C]. The 2020 IEEE International Conference on Artificial Intelligence and Information Systems (ICAIIS), Dalian, China, 2020: 221–224.
[12]	LI Weiwei, WANG Ning, JIAO Long, et al. Physical layer spoofing attack detection in MmWave massive MIMO 5G networks[J]. IEEE Access, 2021, 9: 60419–60432. doi: 10.1109/ACCESS.2021.3073115
[13]	WANG Ning, JIAO Long, WANG Pu, et al. Exploiting beam features for spoofing attack detection in mmWave 60-GHz IEEE 802.11ad networks[J]. IEEE Transactions on Wireless Communications, 2021, 20(5): 3321–3335. doi: 10.1109/TWC.2021.3049160
[14]	BALAKRISHNAN S, GUPTA S, BHUYAN A, et al. Physical layer identification based on spatial–temporal beam features for millimeter-wave wireless networks[J]. IEEE Transactions on Information Forensics and Security, 2020, 15: 1831–1845. doi: 10.1109/TIFS.2019.2948283
[15]	HEMADEH I A, SATYANARAYANA K, EL-HAJJAR M, et al. Millimeter-wave communications: Physical channel models, design considerations, antenna constructions, and link-budget[J]. IEEE Communications Surveys & Tutorials, 2018, 20(2): 870–913. doi: 10.1109/COMST.2017.2783541
[16]	JU Shihao and RAPPAPORT T S. Millimeter-wave extended NYUSIM channel model for spatial consistency[C]. The 2018 IEEE Global Communications Conference (GLOBECOM), Abu Dhabi, United Arab Emirates, 2018: 1–6.
[17]	LIM Y G, CHO Y J, SIM M S, et al. Map-based millimeter-wave channel models: An overview, guidelines, and data[EB/OL]. http://arxiv.org/abs/1711.09052, 2017.
[18]	GOWER J C and LEGENDRE P. Metric and Euclidean properties of dissimilarity coefficients[J]. Journal of Classification, 1986, 3(1): 5–48. doi: 10.1007/BF01896809
[19]	卜凡鹏, 陈俊艺, 张琪祁, 等. 一种基于双层迭代聚类分析的负荷模式可控精细化识别方法[J]. 电网技术, 2018, 42(3): 903–910. doi: 10.13335/j.1000-3673.pst.2017.1397 BU Fanpeng, CHEN Junyi, ZHANG Qiqi, et al. A controllable refined recognition method of electrical load pattern based on bilayer iterative clustering analysis[J]. Power System Technology, 2018, 42(3): 903–910. doi: 10.13335/j.1000-3673.pst.2017.1397
[20]	YOU Yang, DEMMEL J, CZECHOWSKI K, et al. CA-SVM: Communication-avoiding support vector machines on distributed systems[C]. The 2015 IEEE International Parallel and Distributed Processing Symposium, Hyderabad, India, 2015: 847–859.
[21]	SINHASHTHITA W and JEARANAITANAKIJ K. Improving KNN algorithm based on weighted attributes by Pearson correlation coefficient and PSO fine tuning[C]. The 5th International Conference on Information Technology (InCIT), Chonburi, Thailand, 2020: 27–32.
[22]	XIAO L, GREENSTEIN L, MANDAYAM N, et al. Fingerprints in the ether: Using the physical layer for wireless authentication[C]. The 2007 IEEE International Conference on Communications, Glasgow, UK, 2007: 4646–4651.