Physical Layer Spoofing Attack Detection in MmWave Massive MIMO 5G Networks

Abstract

Identity spoofing attacks pose one of the most serious threats to wireless networks, where the attacker can masquerade as legitimate users by modifying its own identity. Channel-based physicallayer security is a promising technology to counter identity spoofing attacks. Although various channelbased security technologies have been proposed, the study of channel-based spoofing attack detection in 5G networks is largely open. This paper introduces a new channel-based spoofing attack detection scheme based on channel virtual (or called beamspace) representation in millimeter wave (mmWave) massive multipleinput and multiple-output (MIMO) 5G networks. The principal components of channel virtual representation (PC-CVR) are extracted as a new channel feature. Compared with traditional channel features, the proposed features can be more sensitive to the location of transmitters and more suitable to mmWave 5G networks. Based on PC-CVR, we offer two detection strategies to achieve the spoofing attack detection tackling static and dynamic radio environments, respectively. For the static radio environment where the channel correlation is stable, Neyman-Pearson (NP) testing-based spoofing attack detection is provided depending on the 2 -norm of PC-CVR. For the dynamic radio environment where the channel correlation is changing, the problem of spoofing attack detection is transformed into a one-class classification problem. To efficiently handle this problem, an online detection framework based on a feedforward neural network with a single hidden layer is presented. Simulation results evaluate and confirm the effectiveness of the proposed detection schemes. For the static radio environment, the detection rate can be improved around 25% with the help of PC-CVR under the NP testing-based detection, and the detection accuracy can reach 99% with the machine learning-based scheme under the dynamic radio environment. INDEX TERMSPhysical layer security, Spoofing attack detection, MmWave communication, Virtual channel.

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“…In [266], the authors consider a mmWave MIMO scenario where channel characteristics are highly sensitive to spatial location. For this purpose, the authors propose a novel channel feature to improve the detection accuracy of mmWave MIMO networks.…”

Physical layer authentication for 5G/6G millimeter wave communications by using channel sparsity

“…For instance, it is still not clear that how does the sparse properties of mmWave channel can benefit the PLA detections and how to design the lightweight but effective detection criteria to achieve the desirable performance. Recently, works [21,22] utilized machine learning algorithms to "learn" the variations of sparsity of the mmWave channel, which can further improve the detection performance. However, utilizing such learning algorithms usually requires an additional training phase, which increases the overall delay and complexity.…”

“…Therefore, the complexity order of Algorithm 1 can be approximated as $$\mathcal {O}(N^2)$$. Based on the analysis above, in comparison to the machine leaning based schemes [21, 22], the complexity of Algorithm 1 is rather low.…”

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“…Gerrit et al [ 35 ] studied possible threats according to the STRIDE threat classification model and derive a risk matrix based on the likelihood and impact of 12 threat scenarios that affect the radio access and the network core. Sullivan et al [ 36 ] categorize security technologies using Open Systems Interconnection (OSI) layers and, for each layer, the authors discuss vulnerabilities, threats, security solutions, challenges, gaps, and open research issues. Weiwei et al [ 37 ] proposed a new channel-based spoofing attack detection scheme in millimeter-wave massive multiple-input multiple-output (mmWave-MIMO) 5G networks using channel virtual representation.…”