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Active Spoofing Attack Detection: An Eigenvalue Distribution and Forecasting Approach

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

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Active Spoofing Attack Detection: An Eigenvalue Distribution and Forecasting Approach. / Gao, N.; Jing, X.; Ni, Q. et al.
2018 IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC). IEEE, 2018. p. 1-6.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Gao, N, Jing, X, Ni, Q & Su, B 2018, Active Spoofing Attack Detection: An Eigenvalue Distribution and Forecasting Approach. in 2018 IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC). IEEE, pp. 1-6. https://doi.org/10.1109/PIMRC.2018.8580677

APA

Gao, N., Jing, X., Ni, Q., & Su, B. (2018). Active Spoofing Attack Detection: An Eigenvalue Distribution and Forecasting Approach. In 2018 IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) (pp. 1-6). IEEE. https://doi.org/10.1109/PIMRC.2018.8580677

Vancouver

Gao N, Jing X, Ni Q, Su B. Active Spoofing Attack Detection: An Eigenvalue Distribution and Forecasting Approach. In 2018 IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC). IEEE. 2018. p. 1-6 doi: 10.1109/PIMRC.2018.8580677

Author

Gao, N. ; Jing, X. ; Ni, Q. et al. / Active Spoofing Attack Detection : An Eigenvalue Distribution and Forecasting Approach. 2018 IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC). IEEE, 2018. pp. 1-6

Bibtex

@inproceedings{b8f6d2e5ab6e431fa11b08b5ca2b2898,
title = "Active Spoofing Attack Detection: An Eigenvalue Distribution and Forecasting Approach",
abstract = "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.",
keywords = "Bayes methods, computer network security, eigenvalues and eigenfunctions, matrix algebra, signal detection, statistical distributions, biased estimation, actual signal, closed-form expressions, detection probability, false alarm probability, Neyman-Pearson threshold, eigenvalue distribution, channel time-varying, Bayesian forecasting, active spoofing attack detection, forecasting approach, physical-layer security, ever-increasing attention, secure communication, HPN-to-devices network, MAC spoofing attack, malicious attack, eigenvalue test, random matrix theory algorithm, prior information, sampled signal, HTD, ETRMT, Eigenvalues and eigenfunctions, Covariance matrices, Security, Receivers, Antenna arrays, Forecasting, Eavesdropping, Active MAC spoofing attack detection, random matrix theory",
author = "N. Gao and X. Jing and Q. Ni and B. Su",
note = "{\textcopyright}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.",
year = "2018",
month = dec,
day = "20",
doi = "10.1109/PIMRC.2018.8580677",
language = "English",
pages = "1--6",
booktitle = "2018 IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)",
publisher = "IEEE",

}

RIS

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 -