Active Eavesdropping Detection Based on Large-Dimensional Random Matrix Theory for Massive MIMO-Enabled IoT

Abstract

The increasing Internet-of-Things (IoT) applications will take a significant share of the services of the fifth generation mobile network (5G). However, IoT devices are vulnerable to security threats due to the limitation of their simple hardware and communication protocol. Massive multiple-input multiple-output (massive MIMO) is recognized as a promising technique to support massive connections of IoT devices, but it faces potential physical layer breaches. An active eavesdropper can compromises the communication security of massive MIMO systems by purposely contaminating the uplink pilots. According to the random matrix theory (RMT), the eigenvalue distribution of a large dimensional matrix composed of data samples converges to the limit spectrum distribution that can be characterized by matrix dimensions. With the assistance of RMT, we propose an active eavesdropping detection method in this paper. The theoretical limit spectrum distribution is exploited to determine the distribution range of the eigenvalues of a legitimate user signal. In addition the noise components are removed using the Marčenko–Pastur law of RMT. Hypothesis testing is then carried out to determine whether the spread range of eigenvalues is “normal” or not. Simulation results show that, compared with the classical Minimum Description Length (MDL)-based detection algorithm, the proposed method significantly improves active eavesdropping detection performance.