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In a time-division duplex (TDD) multiple antenna system, the channel state information (CSI) can be estimated using reverse training. A pilot contamination (spoofing) attack occurs when during the training phase, an adversary also sends identical training (pilot) signal as that of the legitimate receiver. This contaminates channel estimation and alters the legitimate beamforming design, facilitating eavesdropping. Most of past approaches to pilot spoofing detection are limited to flat fading channels. A recent approach proposed superimposing a random sequence on the training sequence at the legitimate receiver for detection of pilot spoofing attack over frequency selective channels, with unknown channels and channel lengths, except that an upper bound on the number of channel taps is assumed to be known. In this paper we augment this approach with joint estimation of both legitimate receiver and eavesdropper channels, and secure time-reversal precoding, to mitigate the effects of pilot spoofing. The proposed mitigation approach is illustrated via simulations. 

In a time-division duplex (TDD) multiple antenna system, the channel state information (CSI) can be estimated using reverse training. A pilot contamination (spoofing) attack occurs when during the training phase, an adversary also sends identical training (pilot) signal as that of the legitimate receiver. This contaminates channel estimation and alters the legitimate precoder/beamformimg design, facilitating eavesdropping. Past approaches to pilot spoofing detection are limited to flat fading channels. In this paper we propose a novel approach for detection of pilot spoofing attack over frequency selective channels, with unknown channels and channel lengths, except that an upperbound on the number of channel taps is assumed to be known. The proposed approach is illustrated by numerical examples and they show the efficacy of the proposed approach. A method to estimate Bob's channel regardless of the spoofing attack, is also presented and illustrated via simulations. 

In a time-division duplex (TDD) multiple antenna system the channel state information (CSI) can be estimated using reverse training. In multicell multiuser massive MIMO systems, pilot contamination degrades CSI estimation performance and adversely affects massive MIMO system performance. In this paper we consider a subspace-based semi-blind approach where we have training data as well as information bearing data from various users (both in-cell and neighboring cells) at the base station (BS). Existing subspace approaches assume that the interfering users from neighboring cells are always at distinctly lower power levels at the BS compared to the in-cell users. In this paper we do not make any such assumption. Unlike existing approaches, the BS estimates the channels of all users: in-cell and significant neighboring cell users, i.e., ones with comparable power levels at the BS. We exploit both subspace method using correlation as well as blind source separation using higher-order statistics. The proposed approach is illustrated via simulation examples. 

In a time-division duplex (TDD) multiple antenna system, the channel state information (CSI) can be estimated using reverse training. A pilot contamination (spoofing) attack occurs when during the training phase, an adversary (spoofer) also sends identical training (pilot) signal as that of the legitimate receiver. This contaminates channel estimation and alters the legitimate beamforming design, facilitating eavesdropping. A recent approach proposed superimposing a random sequence on the training sequence at the legitimate receiver and then using the minimum description length (MDL) criterion to detect pilot contamination attack. In this paper we augment this approach with joint estimation of both legitimate receiver and eavesdropper channels, and secure beamforming, to mitigate the effects of pilot spoofing. We consider two cases: (i) the spoofer transmits only the pilot signal, (ii) the spoofer also adds a random sequence to its pilot. The proposed mitigation approach is illustrated via simulations. 

In a time-division duplex (TDD) multiple antenna system, the channel state information (CSI) can be estimated using reverse training. A pilot spoofing attack occurs when during the training phase, an adversary (spoofer) also sends identical training (pilot) signal as that of the legitimate receiver. This contaminates channel estimation and alters the legitimate precoder design, facilitating eavesdropping. A recent approach proposed superimposing a random sequence on the training sequence at the legitimate receivers, and then using the minimum description length (MDL) criterion to detect pilot spoofing attack via source enumeration. In this letter, we extend this approach by exploiting temporal subspace properties of the pilot signals in conjunction with the MDL criterion, to determine which pilots are contaminated by a spoofer, and which ones are free of spoofing attack. The identification performance is illustrated via simulations. 

We consider detection of spoofing relay attack in time-division duplex (TDD) multiple antenna systems where an adversary operating in a full-duplex mode, amplifies and forwards the training signal of the legitimate receiver. In TDD systems, the channel state information (CSI) can be acquired using reverse training. The spoofing relay attack contaminates the channel estimation phase. Consequently the beamformer designed using the contaminated channel estimate can lead to a significant information leakage to the attacking adversary. A recent approach proposed using the minimum description length (MDL) criterion to detect spoofing relay attack. In this paper we augment this approach with joint channel estimation and secure beamforming to mitigate the effects of pilot contamination by spoofing relay. The proposed mitigation approach is illustrated via simulations. 

In a time-division duplex (TDD) multiple antenna system, the channel state information (CSI) can be estimated using reverse training. A pilot contamination (spoofing) attack occurs when during the training phase, an adversary also sends identical training (pilot) signal as that of the legitimate receiver. This contaminates channel estimation and alters the legitimate beamformimg design, facilitating eavesdropping. A recent approach proposed superimposing a random sequence on the training sequence at the legitimate receiver and then using the minimum description length (MDL) criterion to detect pilot contamination attack. In this paper we augment this approach with joint estimation of both legitimate receiver and eavesdropper channels, and secure beamforming, to mitigate the effects of pilot spoofing. The proposed mitigation approach is illustrated via simulations.