The Experts below are selected from a list of 3843 Experts worldwide ranked by ideXlab platform
Kai Zeng - One of the best experts on this subject based on the ideXlab platform.
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physical layer Spoofing Attack detection in mmwave massive mimo 5g networks
IEEE Access, 2021Co-Authors: Ning Wang, Long Jiao, Kai ZengAbstract: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.
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exploiting beam features for Spoofing Attack detection in mmwave 60 ghz ieee 802 11ad networks
IEEE Transactions on Wireless Communications, 2021Co-Authors: Ning Wang, Long Jiao, P Wang, Kai ZengAbstract:Spoofing Attacks pose a serious threat to wireless communications. Exploiting physical-layer features to counter Spoofing Attacks is a promising solution. Although various physical-layer Spoofing Attack detection (PL-SAD) techniques have been proposed for conventional 802.11 networks in the sub-6GHz band, the study of PL-SAD for 802.11ad networks in 5G millimeter wave (mmWave) 60GHz band is largely open. In this paper, to achieve efficient PL-SAD in 5G networks, we propose a unique physical layer feature in IEEE 802.11ad networks, i.e., the signal-to-noise-ratio (SNR) trace obtained at the receiver in the sector level sweep (SLS) process. The SNR trace is readily extractable from the off-the-shelf device, and it is dependent on both transmitter location and intrinsic hardware impairment. Therefore, it can be used to achieve an efficient detection no matter the Attacker is co-located with the legitimate transmitter or not. To achieve Spoofing Attack detection, we provide two methods based on different machine learning models. For the first method, the detection problem is formulated as a machine learning classification problem. To tackle the small sample learning and fast model construction challenges, we propose a novel neural network framework consisting of a backpropation network, a forward propagation network, and generative adversarial networks (GANs). Another method involves a Siamese network, in which the similarity between sample pairs from one device is used to achieve PL-SAD. It can tackle the training problem that the historical data cannot support the identification of the same device in a new communication session. We conduct experiments using off-the-shelf 802.11ad devices, Talon AD7200s and MG360, to evaluate the performance of the proposed PL-SAD schemes. Experimental results confirm the effectiveness of the proposed PL-SAD schemes, and the detection accuracy can reach 99% using small sample sizes under different scenarios.
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Efficient Identity Spoofing Attack Detection for IoT in mm-Wave and Massive MIMO 5G Communication
2018 IEEE Global Communications Conference (GLOBECOM), 2018Co-Authors: Ning Wang, Long Jiao, Pu Wang, Monireh Dabaghchian, Kai ZengAbstract:In many IoT (Internet-of-Things) applications, a large number of low-cost IoT devices are connected to the Internet through an access point (AP) or gateway via wireless communication. Due to the resource constraints on IoT devices and broadcast nature of wireless medium, identity Spoofing Attacks are easy to launch but hard to defend in an IoT wireless access network. In this paper, under the context of 5G communication, we propose an efficient physical layer identity Spoofing Attack detection scheme for IoT. By harnessing the sparsity of the virtual channel in mmWave and Massive MIMO 5G communication, we propose a two- step detection scheme. In the first step, our scheme detects anomalies by examining the virtual angles of arrival (AoA) and path gains of all the IoT devices simultaneously in a virtual channel space (VCS). In the second step, we introduce a machine learning based detection scheme to detect the actual Attack. Simulation results evaluate and confirm the effectiveness of the proposed detection scheme. The minimum Bayes risk of the proposed scheme can be less than 0.5\% even in the presence of 100 IoT devices.
Bangning Zhang - One of the best experts on this subject based on the ideXlab platform.
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secrecy analysis and active pilot Spoofing Attack detection for multigroup multicasting cell free massive mimo systems
IEEE Access, 2019Co-Authors: Xianyu Zhang, Daoxing Guo, Zhiguo Ding, Bangning ZhangAbstract:In this paper, we investigate the secure transmission in multigroup multicasting cell-free massive MIMO system in the presence of pilot Spoofing Attack. With the imperfect uplink and downlink channel estimation, a distributed conjugate beamforming processing with normalized power constraint policy is exploited at access points (APs) for downlink multicasting data transmission. Closed-form expressions for the per-user achievable rate are derived with and without downlink training, respectively. Also, the analytical results of the upper bound on the information leakage to eavesdropper are carried out. Moreover, a mechanism based on the minimum description length (MDL) is presented to detect pilot Spoofing Attack. Consequently, the achievable ergodic secrecy rate is obtained to evaluate the system's secrecy performance. The numerical results are presented to quantitatively analyze the impacts of eavesdropper's Spoofing pilot power and the number of groups on the secrecy performance of the considered systems.
Xianyu Zhang - One of the best experts on this subject based on the ideXlab platform.
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secrecy analysis and active pilot Spoofing Attack detection for multigroup multicasting cell free massive mimo systems
IEEE Access, 2019Co-Authors: Xianyu Zhang, Daoxing Guo, Zhiguo Ding, Bangning ZhangAbstract:In this paper, we investigate the secure transmission in multigroup multicasting cell-free massive MIMO system in the presence of pilot Spoofing Attack. With the imperfect uplink and downlink channel estimation, a distributed conjugate beamforming processing with normalized power constraint policy is exploited at access points (APs) for downlink multicasting data transmission. Closed-form expressions for the per-user achievable rate are derived with and without downlink training, respectively. Also, the analytical results of the upper bound on the information leakage to eavesdropper are carried out. Moreover, a mechanism based on the minimum description length (MDL) is presented to detect pilot Spoofing Attack. Consequently, the achievable ergodic secrecy rate is obtained to evaluate the system's secrecy performance. The numerical results are presented to quantitatively analyze the impacts of eavesdropper's Spoofing pilot power and the number of groups on the secrecy performance of the considered systems.
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secure communication in multigroup multicasting cell free massive mimo networks with active Spoofing Attack
Electronics Letters, 2019Co-Authors: Xianyu Zhang, Daoxing GuoAbstract:The authors investigate the secure communication in multigroup multicasting cell-free massive MIMO systems in the presence of an active pilot Spoofing Attack. With the imperfect estimated local channels at access points, a distributed conjugate beamforming with normalised power constraint policy is exploited for downlink multicasting transmission. Closed-form expressions for the per-user achievable rate and information leakage to eavesdropper are derived, respectively. Numerical results are presented to verify the correctness of their analytical results and quantitatively analyse the impacts of eavesdropper's Spoofing pilot power and a number of groups on the secrecy performance of the considered systems.
Ning Wang - One of the best experts on this subject based on the ideXlab platform.
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physical layer Spoofing Attack detection in mmwave massive mimo 5g networks
IEEE Access, 2021Co-Authors: Ning Wang, Long Jiao, Kai ZengAbstract: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.
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exploiting beam features for Spoofing Attack detection in mmwave 60 ghz ieee 802 11ad networks
IEEE Transactions on Wireless Communications, 2021Co-Authors: Ning Wang, Long Jiao, P Wang, Kai ZengAbstract:Spoofing Attacks pose a serious threat to wireless communications. Exploiting physical-layer features to counter Spoofing Attacks is a promising solution. Although various physical-layer Spoofing Attack detection (PL-SAD) techniques have been proposed for conventional 802.11 networks in the sub-6GHz band, the study of PL-SAD for 802.11ad networks in 5G millimeter wave (mmWave) 60GHz band is largely open. In this paper, to achieve efficient PL-SAD in 5G networks, we propose a unique physical layer feature in IEEE 802.11ad networks, i.e., the signal-to-noise-ratio (SNR) trace obtained at the receiver in the sector level sweep (SLS) process. The SNR trace is readily extractable from the off-the-shelf device, and it is dependent on both transmitter location and intrinsic hardware impairment. Therefore, it can be used to achieve an efficient detection no matter the Attacker is co-located with the legitimate transmitter or not. To achieve Spoofing Attack detection, we provide two methods based on different machine learning models. For the first method, the detection problem is formulated as a machine learning classification problem. To tackle the small sample learning and fast model construction challenges, we propose a novel neural network framework consisting of a backpropation network, a forward propagation network, and generative adversarial networks (GANs). Another method involves a Siamese network, in which the similarity between sample pairs from one device is used to achieve PL-SAD. It can tackle the training problem that the historical data cannot support the identification of the same device in a new communication session. We conduct experiments using off-the-shelf 802.11ad devices, Talon AD7200s and MG360, to evaluate the performance of the proposed PL-SAD schemes. Experimental results confirm the effectiveness of the proposed PL-SAD schemes, and the detection accuracy can reach 99% using small sample sizes under different scenarios.
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Efficient Identity Spoofing Attack Detection for IoT in mm-Wave and Massive MIMO 5G Communication
2018 IEEE Global Communications Conference (GLOBECOM), 2018Co-Authors: Ning Wang, Long Jiao, Pu Wang, Monireh Dabaghchian, Kai ZengAbstract:In many IoT (Internet-of-Things) applications, a large number of low-cost IoT devices are connected to the Internet through an access point (AP) or gateway via wireless communication. Due to the resource constraints on IoT devices and broadcast nature of wireless medium, identity Spoofing Attacks are easy to launch but hard to defend in an IoT wireless access network. In this paper, under the context of 5G communication, we propose an efficient physical layer identity Spoofing Attack detection scheme for IoT. By harnessing the sparsity of the virtual channel in mmWave and Massive MIMO 5G communication, we propose a two- step detection scheme. In the first step, our scheme detects anomalies by examining the virtual angles of arrival (AoA) and path gains of all the IoT devices simultaneously in a virtual channel space (VCS). In the second step, we introduce a machine learning based detection scheme to detect the actual Attack. Simulation results evaluate and confirm the effectiveness of the proposed detection scheme. The minimum Bayes risk of the proposed scheme can be less than 0.5\% even in the presence of 100 IoT devices.
Daoxing Guo - One of the best experts on this subject based on the ideXlab platform.
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secrecy analysis and active pilot Spoofing Attack detection for multigroup multicasting cell free massive mimo systems
IEEE Access, 2019Co-Authors: Xianyu Zhang, Daoxing Guo, Zhiguo Ding, Bangning ZhangAbstract:In this paper, we investigate the secure transmission in multigroup multicasting cell-free massive MIMO system in the presence of pilot Spoofing Attack. With the imperfect uplink and downlink channel estimation, a distributed conjugate beamforming processing with normalized power constraint policy is exploited at access points (APs) for downlink multicasting data transmission. Closed-form expressions for the per-user achievable rate are derived with and without downlink training, respectively. Also, the analytical results of the upper bound on the information leakage to eavesdropper are carried out. Moreover, a mechanism based on the minimum description length (MDL) is presented to detect pilot Spoofing Attack. Consequently, the achievable ergodic secrecy rate is obtained to evaluate the system's secrecy performance. The numerical results are presented to quantitatively analyze the impacts of eavesdropper's Spoofing pilot power and the number of groups on the secrecy performance of the considered systems.
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secure communication in multigroup multicasting cell free massive mimo networks with active Spoofing Attack
Electronics Letters, 2019Co-Authors: Xianyu Zhang, Daoxing GuoAbstract:The authors investigate the secure communication in multigroup multicasting cell-free massive MIMO systems in the presence of an active pilot Spoofing Attack. With the imperfect estimated local channels at access points, a distributed conjugate beamforming with normalised power constraint policy is exploited for downlink multicasting transmission. Closed-form expressions for the per-user achievable rate and information leakage to eavesdropper are derived, respectively. Numerical results are presented to verify the correctness of their analytical results and quantitatively analyse the impacts of eavesdropper's Spoofing pilot power and a number of groups on the secrecy performance of the considered systems.
