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Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - Active Spoofing Attack Detection
T2 - An Eigenvalue Distribution and Forecasting Approach
AU - Gao, N.
AU - Jing, X.
AU - Ni, Q.
AU - Su, B.
N1 - ©2018 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
PY - 2018/12/20
Y1 - 2018/12/20
N2 - Physical-layer security has drawn ever-increasing attention in the next generation wireless communications. In this paper, we focus on studying the secure communication in an HPN-to-devices (HTD) network, in which a new type of MAC spoofing attack is considered. To detect the malicious attack, we propose a novel algorithm, namely, eigenvalue test using random matrix theory (ETRMT) algorithm, which needs no prior information about the channel. In particular, when the number of samples is finite at the receiver or the number of devices is large, the sampled signal is the biased estimation of the actual signal, which inspires us to use the random matrix theory to analyze the spoofing attack detection. The closed-form expressions of the detection probability, the false alarm probability, and the Neyman-Pearson threshold are derived based on eigenvalue distribution of the spiked population model. In addition, taking the channel time-varying into consideration, we provide an adaptive threshold tracking method by using Bayesian forecasting. Finally, the simulations are conducted to validate our proposed method and some insightful conclusions are obtained.
AB - Physical-layer security has drawn ever-increasing attention in the next generation wireless communications. In this paper, we focus on studying the secure communication in an HPN-to-devices (HTD) network, in which a new type of MAC spoofing attack is considered. To detect the malicious attack, we propose a novel algorithm, namely, eigenvalue test using random matrix theory (ETRMT) algorithm, which needs no prior information about the channel. In particular, when the number of samples is finite at the receiver or the number of devices is large, the sampled signal is the biased estimation of the actual signal, which inspires us to use the random matrix theory to analyze the spoofing attack detection. The closed-form expressions of the detection probability, the false alarm probability, and the Neyman-Pearson threshold are derived based on eigenvalue distribution of the spiked population model. In addition, taking the channel time-varying into consideration, we provide an adaptive threshold tracking method by using Bayesian forecasting. Finally, the simulations are conducted to validate our proposed method and some insightful conclusions are obtained.
KW - Bayes methods
KW - computer network security
KW - eigenvalues and eigenfunctions
KW - matrix algebra
KW - signal detection
KW - statistical distributions
KW - biased estimation
KW - actual signal
KW - closed-form expressions
KW - detection probability
KW - false alarm probability
KW - Neyman-Pearson threshold
KW - eigenvalue distribution
KW - channel time-varying
KW - Bayesian forecasting
KW - active spoofing attack detection
KW - forecasting approach
KW - physical-layer security
KW - ever-increasing attention
KW - secure communication
KW - HPN-to-devices network
KW - MAC spoofing attack
KW - malicious attack
KW - eigenvalue test
KW - random matrix theory algorithm
KW - prior information
KW - sampled signal
KW - HTD
KW - ETRMT
KW - Eigenvalues and eigenfunctions
KW - Covariance matrices
KW - Security
KW - Receivers
KW - Antenna arrays
KW - Forecasting
KW - Eavesdropping
KW - Active MAC spoofing attack detection
KW - random matrix theory
U2 - 10.1109/PIMRC.2018.8580677
DO - 10.1109/PIMRC.2018.8580677
M3 - Conference contribution/Paper
SP - 1
EP - 6
BT - 2018 IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)
PB - IEEE
ER -