Massive multiple-input multiple-output (MMIMO) is essential to modern wireless communication systems, like 5G and 6G, but it is vulnerable to active eavesdropping attacks. One type of such attack is the pilot contamination attack (PCA), where a malicious user copies pilot signals from an authentic user during uplink, intentionally interfering with the base station's (BS) channel estimation accuracy. In this work, we propose to use a Decision Tree (DT) algorithm for PCA detection at the BS in a multi-user system. We present a methodology to generate training data for the DT classifier and select the best DT according to their depth. Then, we simulate different scenarios that could be encountered in practice and compare the DT to a classical technique based on likelihood ratio testing (LRT) submitted to the same scenarios. The results revealed that a DT with only one level of depth is sufficient to outperform the LRT. The DT shows a good performance regarding the probability of detection in noisy scenarios and when the malicious user transmits with low power, in which case the LRT fails to detect the PCA. We also show that the reason for the good performance of the DT is its ability to compute a threshold that separates PCA data from non-PCA data better than the LRT's threshold. Moreover, the DT does not necessitate prior knowledge of noise power or assumptions regarding the signal power of malicious users, prerequisites typically essential for LRT and other hypothesis testing methodologies.

翻译：大规模多输入多输出（MMIMO）是现代无线通信系统（如5G和6G）的关键技术，但其易受主动窃听攻击。其中一类攻击是导频污染攻击（PCA），即恶意用户在上行链路中复制合法用户的导频信号，故意干扰基站（BS）的信道估计精度。本研究提出在多用户系统中采用决策树（DT）算法于基站端进行PCA检测。我们提出了一种为DT分类器生成训练数据的方法，并根据树深度选择最优DT。随后，我们模拟了实际可能遇到的不同场景，并将DT与基于似然比检验（LRT）的经典技术在相同场景下进行对比。结果表明，仅需单层深度的DT即可超越LRT的性能。在噪声场景及恶意用户以低功率传输的情况下（此时LRT无法检测PCA），DT在检测概率方面表现出良好性能。我们还证明DT性能优异的原因在于其计算出的阈值能比LRT阈值更有效地区分PCA数据与非PCA数据。此外，DT无需预先获知噪声功率或对恶意用户信号功率进行假设，而这些前提条件通常对LRT及其他假设检验方法至关重要。