Grant-free random access is an emerging technology for providing massive connectivity for 5G massive machine-type communications (mMTC), where non-orthogonal pilot sequences are used to simultaneously detect active users and estimate channels. However, grant-free 5G IoT networks are vulnerable to pilot contamination attacks (PCA), where the attacker can send the same pilots as legitimate IoT users to harm the active user detection and channel estimation. To defend against this attack, in this article, we propose a physical-layer countermeasure based on the channel virtual representation (CVR). CVR can emphasize the unique characteristics of mmWave channels that are sensitive to the location of the sender. This can be utilized to counter PCA no matter if the attacker's pilots are superimposed to that of the victim or not. Based on this observation, to achieve an efficient PCA detection, a single-hidden-layer multiple measurement (SHMM) Siamese network is employed. This solution tackles the challenges of channel randomness and massive connectivity in mMTC IoT networks, and supports small sample learning. Simulation results evaluate and confirm the effectiveness of the proposed detection scheme under various scenarios. The detection accuracy can approach 99% with 128 antennas at the receiver and reach above 95% even with only 50 training samples.

中文翻译：

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5G 毫米波无赠款物联网网络的试点污染攻击检测


无授权随机接入是一种为 5G 大规模机器类型通信 (mMTC) 提供大规模连接的新兴技术，其中使用非正交导频序列来同时检测活动用户和估计信道。然而，无授权的 5G 物联网网络很容易受到导频污染攻击 (PCA)，攻击者可以发送与合法物联网用户相同的导频来损害活跃用户检测和信道估计。为了防御这种攻击，在本文中，我们提出了一种基于通道虚拟表示（CVR）的物理层对策。 CVR 可以强调毫米波通道对发送者位置敏感的独特特性。无论攻击者的飞行员是否叠加到受害者的飞行员，这都可以用来对抗 PCA。基于这一观察，为了实现有效的 PCA 检测，采用了单隐藏层多重测量（SHMM）暹罗网络。该解决方案解决了 mMTC 物联网网络中信道随机性和海量连接的挑战，并支持小样本学习。仿真结果评估并证实了所提出的检测方案在各种场景下的有效性。接收端有128个天线时，检测准确率可以接近99%，即使只有50个训练样本，检测准确率也能达到95%以上。