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

CLC Number:

TP18

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.

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