Spoofing Attack Detection Scheme Based on Channel Fingerprint for Millimeter Wave MIMO System
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摘要: 针对毫米波多输入输出系统(MIMO)中的身份欺骗攻击问题，该文提出一种基于信道指纹的攻击检测方案。在波束域中，毫米波信道图样呈现波束的稀疏性和高方向特性，且这种波束域特性与终端位置有极高的相关性。该文将该波束域信道图样作为一种信道指纹，提出了一种基于信道指纹的身份欺骗攻击检测方案，将欺骗攻击中的终端身份认证问题建模成对其信道指纹的二分类问题，并使用基于监督学习的支持向量机算法求解该分类问题。为获得好的分类效果，基于对信道指纹的数值分析，比较了皮尔逊相关系数、余弦相似度、相关矩阵距离、欧氏距离等相似度指标。根据比较结果，选择最优的指标作为分类特征训练分类模型。仿真结果表明，即使在低信噪比条件下，该方案仍具有高认证准确性和鲁棒性。与现有相关机制相比，攻击检测精度显著提高。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.
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