Wireless Device Identification Scheme Based on CSI Feature Fingerprints

doi:10.19678/j.issn.1000-3428.0070198

Abstract

Abstract:

In recent years, wireless networks have been widely used in healthcare, industry, education, and military applications. However, security threats for these networks are increasing. Traditional cryptographic authentication methods have several limitations, including restricted computational resources, vulnerabilities to quantum computing, and susceptibility to tampering. To address these challenges, a device fingerprint verification scheme based on physical layer information is proposed. This scheme leverages fingerprint features derived from Channel State Information (CSI) for device identification to prevent malicious Wi-Fi connections. The proposed scheme considers both stationary and mobile devices with the aim of improving the terminal identification accuracy and stability. For stationary devices with minimal interference in the authentication scenario, the CSI amplitude information matrix is used as the authentication fingerprint. For mobile devices, where the CSI information varies with device movement, the direct extraction of fingerprint information is infeasible. Instead, the fingerprint features are constructed by extracting the I/Q phase errors for device identification. Self-designed One-Class SupportVector Machine (SVM) based on Confidence Level (OSCL) and isolation Forest (iForest) based on Confidence Level (IFCL) models are employed to train the fingerprints generated by the two schemes, enabling accurate identification of the target devices. The scheme achieves identification accuracies of 99% and 74% for stationary and mobile devices, respectively. This scheme effectively complements cryptography-based device identification methods. Additionally, during the training phase, only positive data are utilized to address the unpredictability of abnormal device fingerprint information and enhance robustness.

Key words: wireless network security, wireless device identification, physical layer feature fingerprint, Channel State Information (CSI), machine learning

摘要：

近年来无线网络在医疗、工业、教育、军事等领域得到广泛的应用，但同时也面临着更大的安全威胁。传统的密码学验证存在一系列问题，包括计算资源有限、量子计算威胁和身份验证信息易篡改等。为解决此类问题，提出一种基于物理层信息的设备指纹验证方案，利用基于信道状态信息(CSI)的指纹特征进行设备识别，防止恶意Wi-Fi连接。该方案综合考虑了静止设备和可移动设备两种不同终端状态的情况，旨在解决终端识别精度低和稳定性较差的问题。对于静止设备，由于认证情况的干扰较少，采用CSI幅值信息矩阵作为认证指纹；对于移动设备，由于CSI信息会随设备的移动而发生变化，直接提取指纹信息不再适用，通过提取I/Q相位误差构建特征指纹进行设备识别。采用自主设计的基于置信度的单分类支持向量机(SVM)串联模型(OSCL)、基于置信度的孤立森林(iForest)串联模型(IFCL)模型分别对两种方案构建的指纹进行训练，实现了对目标设备的识别。在静止设备识别中，所提方案准确率达到99%；在移动设备识别中，准确率达到74%。该方案可以起到对基于密码学的设备识别方案很好的补充作用，同时训练阶段仅使用正向数据对模型进行训练，很好地解决了异常设备指纹信息不可预测的情况。

关键词: 无线网络安全, 无线设备身份识别, 物理层特征指纹, 信道状态信息, 机器学习

QI Fengyi, ZHANG Xinyou, FENG Li, XING Huanlai. Wireless Device Identification Scheme Based on CSI Feature Fingerprints[J]. Computer Engineering, 2026, 52(2): 236-244.

齐峰毅, 张新有, 冯力, 邢焕来. 基于CSI特征指纹的无线设备识别方案[J]. 计算机工程, 2026, 52(2): 236-244.

/ Recommend / Download Citations

URL: https://www.ecice06.com/EN/10.19678/j.issn.1000-3428.0070198

https://www.ecice06.com/EN/Y2026/V52/I2/236

Figures/Tables 13

Fig.1 Wireless device identification scheme based on CSI

Fig.2 CSI amplitude information

Fig.3 Original CSI amplitude information

Fig.4 Normalized CSI amplitude information

Fig.5 Normality detection result

Fig.6 Linear detection result

Fig.7 CSI amplitude information after filtering

Fig.8 Comparison before and after phase unwrapping

Fig.9 Test points for stationary device

Fig.10 Experimental situation of stationary device

Fig.11 Experimental situation of mobile device

References 26

1	GUBBI J , BUYYA R , MARUSIC S , et al. Internet of Things (IoT): a vision, architectural elements, and future directions. Future Generation Computer Systems, 2013, 29(7): 1645- 1660. doi: 10.1016/j.future.2013.01.010
2	ALHORAIBI L , ALGHAZZAWI D , ALHEBSHI R , et al. Physical layer authentication in wireless networks-based machine learning approaches. Sensors (Basel), 2023, 23(4): 1814. doi: 10.3390/s23041814
3	PAN F , PANG Z B , WEN H , et al. Threshold-free physical layer authentication based on machine learning for industrial wireless CPS. IEEE Transactions on Industrial Informatics, 2019, 15(12): 6481- 6491. doi: 10.1109/TII.2019.2925418
4	ZHANG J Q , RAJENDRAN S , SUN Z , et al. Physical layer security for the Internet of Things: authentication and key generation. IEEE Wireless Communications, 2019, 26(5): 92- 98. doi: 10.1109/MWC.2019.1800455
5	GHOSE N, LAZOS L, LI M. SFIRE: secret-free-in-band trust establishment for COTS wireless devices[C]//Proceedings of the IEEE Conference on Computer Communications. Honolulu, USA: IEEE Press, 2018: 1529-1537.
6	WANG Q , PANG Z B , LIANG W , et al. Spatiotemporal gradient-based physical-layer authentication enhanced by CSI-to-image transformation for industrial mobile devices. IEEE Transactions on Industrial Informatics, 2024, 20(3): 4236- 4245. doi: 10.1109/TII.2023.3316178
7	RAZA C A, ALSMADI M, IKKI S. Survey of identity-based attacks detection techniques in wireless networks using received signal strength[C]//Proceedings of the IEEE Canadian Conference on Electrical and Computer Engineering (CCECE). Quebec, Canada: IEEE Press, 2018: 1-6.
8	SHAN D , ZENG K , XIANG W D , et al. PHY-CRAM: physical layer challenge-response authentication mechanism for wireless networks. IEEE Journal on Selected Areas in Communications, 2013, 31(9): 1817- 1827. doi: 10.1109/JSAC.2013.130914
9	XIA S D , TAO X F , LI N , et al. Multiple correlated attributes based physical layer authentication in wireless networks. IEEE Transactions on Vehicular Technology, 2021, 70(2): 1673- 1687. doi: 10.1109/TVT.2021.3055563
10	宋宇波, 陈冰, 郑天宇, 等. 基于混合特征指纹的无线设备身份识别方法. 计算机研究与发展, 2021, 58(11): 2374- 2399.
	SONG Y B , CHEN B , ZHENG T Y , et al. Hybrid feature fingerprint-based wireless device identification. Journal of Computer Research and Development, 2021, 58(11): 2374- 2399.
11	DEMIRBAS M, SONG Y. An RSSI-based scheme for sybil attack detection in wireless sensor networks[C]//Proceedings of the International Symposium on a World of Wireless, Mobile and Multimedia Networks. Niagara Falls, USA: IEEE Press, 2006: 564-570.
12	CHEN Y Y , YANG J , TRAPPE W , et al. Detecting and localizing identity-based attacks in wireless and sensor networks. IEEE Transactions on Vehicular Technology, 2010, 59(5): 2418- 2434. doi: 10.1109/TVT.2010.2044904
13	XIE N , CHEN J J , HUANG L . Physical-layer authentication using multiple channel-based features. IEEE Transactions on Information Forensics and Security, 2021, 16, 2356- 2366. doi: 10.1109/TIFS.2021.3054534
14	MAHMOOD A, AMAN W, IQBAL M O, et al. Channel impulse response-based distributed physical layer authentication[C]//Proceedings of the IEEE 85th Vehicular Technology Conference (VTC Spring). Sydney, Australia: IEEE Press, 2017: 1-5.
15	LIU H B , WANG Y , LIU J , et al. Authenticating users through fine-grained channel information. IEEE Transactions on Mobile Computing, 2018, 17(2): 251- 264. doi: 10.1109/TMC.2017.2718540
16	LI X L , HUANG K Z , WANG S Y , et al. A physical layer authentication mechanism for IoT devices. China Communications, 2022, 19(5): 129- 140. doi: 10.23919/JCC.2021.00.014
17	ALI K, LIU A X, WANG W, et al. Keystroke recognition using WiFi signals[C]//Proceedings of the 21st Annual International Conference on Mobile Computing and Networking. New York, USA: ACM, 2015: 90-102.
18	YANG Z , ZHOU Z M , LIU Y H . From RSSI to CSI: indoor localization via channel response. ACM Computing Surveys, 2013, 46(2): 1- 32.
19	刘媛媛, 谢先明, 田宪辉, 等. 高效UKF相位解缠算法. 遥感信息, 2022, 37(2): 60- 69.
	LIU Y Y , XIE X M , TIAN X H , et al. An efficient UKF phase unwrapping algorithm. Remote Sensing Information, 2022, 37(2): 60- 69.
20	QUE Z , LIN C J . One-class SVM probabilistic outputs. IEEE Transactions on Neural Networks and Learning Systems, 2025, 36(4): 6244- 6256. doi: 10.1109/TNNLS.2024.3395148
21	刘旭东, 杨绪兵. L1-OCSVM模型设计及其在林业目标检测中的应用. 计算机工程, 2025, 51(7): 375- 384. doi: 10.19678/j.issn.1000-3428.0068679
	LIU X D , YANG X B . Design of L1-OCSVM model and its application in forestry object detection. Computer Engineering, 2025, 51(7): 375- 384. doi: 10.19678/j.issn.1000-3428.0068679
22	ZHUO Y W, ZHU H Z, XUE H, et al. Perceiving accurate CSI phases with commodity WiFi devices[C]//Proceedings of IEEE Conference on Computer Communications. Atlanta, USA: IEEE Press, 2017: 1-9.
23	HUA J Y, SUN H Y, SHEN Z Y, et al. Accurate and efficient wireless device fingerprinting using channel state information[C]//Proceedings of IEEE Conference on Computer Communications. Honolulu, USA: IEEE Press, 2018: 1700-1708.
24	YAN D W , YAN Y B , YANG P L , et al. Real-time identification of rogue WiFi connections in the wild. IEEE Internet of Things Journal, 2023, 10(7): 6042- 6058. doi: 10.1109/JIOT.2022.3223682
25	BARBARIOL T , SUSTO G A . TiWS-iForest: isolation forest in weakly supervised and tiny ML scenarios. Information Sciences, 2022, 610, 126- 143. doi: 10.1016/j.ins.2022.07.129
26	余长宏, 许孔豪, 张泽, 等. 基于分割点改进孤立森林的网络入侵检测方法. 计算机工程, 2024, 50(6): 148- 156. doi: 10.19678/j.issn.1000-3428.0068055
	YU C H , XU K H , ZHANG Z , et al. Improving network intrusion detection methods in lsolated forests based on split points. Computer Engineering, 2024, 50(6): 148- 156. doi: 10.19678/j.issn.1000-3428.0068055

[1]	CHEN Xianyi, MI Hui, HE Junjie, FU Zhangjie. Traceable Federated Learning Copyright Protection Method Based on Structural Embedding [J]. Computer Engineering, 2026, 52(2): 253-264.
[2]	WU Zixuan, LIU Yinhua. Affective Assessment Based on Dynamic Digital Analysis of Pupil Diameter [J]. Computer Engineering, 2026, 52(2): 110-124.
[3]	ZUO Xinyi, MA Shuangbao, LI Shaoqing, WANG Zhenyu, LIU Wei, ZHANG Yang. MS PUF: Multi-dimensional Synergistic Design of Strong PUF Against Machine Learning Modeling Attacks [J]. Computer Engineering, 2025, 51(8): 62-73.
[4]	WANG Hao, GAO Jintao, WANG Jie. Review of Multi-table Join Order Selection in Databases Based on Machine Learning [J]. Computer Engineering, 2025, 51(7): 31-46.
[5]	DENG Zexian, ZHANG Yungui, ZHANG Lin. Research on Multi-Dimensional Time Series Classification Based on the Pre-Trained Recursive Transformer-Mixer [J]. Computer Engineering, 2025, 51(5): 154-165.
[6]	ZHUANG Ziwei, ZHU Junguo. Vietnamese Text Error Detection Method for Multi-source Text [J]. Computer Engineering, 2025, 51(5): 93-102.
[7]	XU Mingliang, LI Fangyuan, MA Haoran, HE Fei. Spike Sorting Algorithms for Large-Scale Neural Recording [J]. Computer Engineering, 2024, 50(6): 1-34.
[8]	LI Yongfei, LI Mingyang, CHANG Xin, CAO Kexin. Anomaly Detection of IoT Water Quality Monitoring Data Based on Explainable Deep Learning [J]. Computer Engineering, 2024, 50(6): 179-187.
[9]	Yi SUN, Huimei WANG, Ming XIAN, Hang XIANG. Research on Heterogeneous Computing Scheduling Strategy for Kubeflow [J]. Computer Engineering, 2024, 50(2): 25-32.
[10]	ZHANG Cai, MA Ziqiang, YAN Bo. Design of a Machine Learning-Based Sentiment Analysis Model for Government Weibo [J]. Computer Engineering, 2024, 50(12): 386-395.
[11]	CHEN Zhixu, JIN Yanxia, LU Ye, YANG Jing, LIU Yabian, SHI Zhiru. Multi-Precision Clothing Modeling Method Based on Subgraph Convolutional Neural Network [J]. Computer Engineering, 2023, 49(4): 174-181.
[12]	LIU Jinshuo, ZHAN Daiyi, DENG Juan, WANG Lina. Network Intrusion Detection Based on Deep Neural Network and Federated Learning [J]. Computer Engineering, 2023, 49(1): 15-21,30.
[13]	GE Xin, ZOU Futai, GUO Wanda, TAN Yue, LI Linsen. Review on Development of Social Botnets [J]. Computer Engineering, 2022, 48(8): 12-24.
[14]	YU Shasha, NIU Baoning. Detection of Illicit Bitcoin Transaction Based on Transaction Unreliablity [J]. Computer Engineering, 2022, 48(8): 166-172.
[15]	JIN Haibo, ZHAO Xinyue. High-Reliability Intrusion Detection Algorithm Under Conformal Prediction Framework [J]. Computer Engineering, 2022, 48(7): 130-140.

Please choose a citation manager

Content to export