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 physical-layer security is a promising technology to counter identity spoofing attacks. Although various channel-based 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 multiple-input 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 ${\ell _{2}}$ -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.
Highlights
Due to its extensive applications, the fifth-generation (5G) wireless networks will have a significant impact on people’s modern lives
MOTIVATION Inspired by the emerging signal processing technology in millimeter Wave (mmWave) communication, i.e., channel virtual representation, we propose to introduce principal components of channel virtual representation (PC-CVR) to achieve the channel-based spoofing attack detection in 5G communications
The number of scatters is set as L and each scatter is assumed to contribute to a single propagation path [36]
Summary
Due to its extensive applications, the fifth-generation (5G) wireless networks will have a significant impact on people’s modern lives. Owing to the broadcast characteristics of wireless communications, 5G wireless networks are vulnerable to physical layer security threats, such as identity spoofing attacks [2], [3] In this attack, the attacker can pretend to be a legitimate user using a faked identity, such as media access control (MAC) address and internet protocol (IP) address, it may gain illegal benefits to further perform advanced attacks, like man-in-the-middle attacks and denial-of-service attacks [4]. When the sample distribution is hard to obtain and the channel correlation parameter is changing due to the dynamic radio environment, to gain a desirable detection performance based on NP testing is struggling To fill this gap, in this paper, we propose to introduce a channel virtual/beamspace representation in mmWave Massive MIMO to achieve the spoofing attack detection in 5G wireless networks.
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