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Impersonation detection in line-of-sight underwater acoustic sensor networks

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Impersonation detection in line-of-sight underwater acoustic sensor networks. / Aman, W.; Rahman, M.M.U.; Qadir, J.; Pervaiz, Haris; Ni, Qiang.

In: IEEE Access, Vol. 6, 06.08.2018, p. 44459-44472.

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Aman, W. ; Rahman, M.M.U. ; Qadir, J. ; Pervaiz, Haris ; Ni, Qiang. / Impersonation detection in line-of-sight underwater acoustic sensor networks. In: IEEE Access. 2018 ; Vol. 6. pp. 44459-44472.

Bibtex

@article{6702013050284bcd93e2462ca3badab1,
title = "Impersonation detection in line-of-sight underwater acoustic sensor networks",
abstract = "This paper considers a line-of-sight underwater acoustic (UWA) sensor network consisting of M underwater sensor nodes randomly deployed according to uniform distribution within a vertical half-disc (the so-called trusted zone). The sensor nodes report their sensed data to a sink node on water surface on a shared UWA reporting channel in a time-division multiple-access fashion, while an active-yet-invisible adversary (so-called Eve) is present in the close vicinity who aims to inject malicious data into the system by impersonating some Alice node. To this end, this paper first considers an additive white Gaussian noise (AWGN) UWA channel, and proposes a novel, multiple-feature-based, two-step method at the sink node to thwart the potential impersonation attack by Eve. Specifically, the sink node exploits the noisy estimates of the distance, the angle of arrival, and the location of the transmit node as device fingerprints to carry out a number of binary hypothesis tests (for impersonation detection) as well as a number of maximum-likelihood (ML) hypothesis tests (for transmitter identification when no impersonation is detected). We provide closed-form expressions for the error probabilities (i.e., the performance) of most of the hypothesis tests. We then consider the case of a UWA with colored noise and frequency-dependent pathloss, and derive a ML distance estimator as well as the corresponding Cramer-Rao bound. We then invoke the proposed two-step, impersonation detection framework by utilizing distance as the sole feature. Finally, we provide detailed simulation results for both AWGN UWA channel and the UWA channel with colored noise. Simulation results verify that the proposed scheme is indeed effective for a UWA channel with the colored noise and frequency-dependent pathloss. {\textcopyright} 2013 IEEE.",
keywords = "hypothesis testing, Impersonation detection, maximum likelihood detection & estimation and Cramer-Rao bound, physical layer authentication, underwater acoustic sensor networks, Acoustic devices, Acoustic noise, Cramer-Rao bounds, Cryptography, Frequency estimation, Gaussian noise (electronic), Jamming, Maximum likelihood estimation, Sensor networks, Sensor nodes, Time division multiple access, White noise, Additive White Gaussian noise, AWGN channel, Binary hypothesis tests, Colored noise, Maximum likelihood hypothesis, Time division multiple accesses (TDMA), Transmitter identification, Underwater acoustic sensor networks, Underwater acoustics",
author = "W. Aman and M.M.U. Rahman and J. Qadir and Haris Pervaiz and Qiang Ni",
year = "2018",
month = aug,
day = "6",
doi = "10.1109/ACCESS.2018.2863945",
language = "English",
volume = "6",
pages = "44459--44472",
journal = "IEEE Access",
issn = "2169-3536",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

RIS

TY - JOUR

T1 - Impersonation detection in line-of-sight underwater acoustic sensor networks

AU - Aman, W.

AU - Rahman, M.M.U.

AU - Qadir, J.

AU - Pervaiz, Haris

AU - Ni, Qiang

PY - 2018/8/6

Y1 - 2018/8/6

N2 - This paper considers a line-of-sight underwater acoustic (UWA) sensor network consisting of M underwater sensor nodes randomly deployed according to uniform distribution within a vertical half-disc (the so-called trusted zone). The sensor nodes report their sensed data to a sink node on water surface on a shared UWA reporting channel in a time-division multiple-access fashion, while an active-yet-invisible adversary (so-called Eve) is present in the close vicinity who aims to inject malicious data into the system by impersonating some Alice node. To this end, this paper first considers an additive white Gaussian noise (AWGN) UWA channel, and proposes a novel, multiple-feature-based, two-step method at the sink node to thwart the potential impersonation attack by Eve. Specifically, the sink node exploits the noisy estimates of the distance, the angle of arrival, and the location of the transmit node as device fingerprints to carry out a number of binary hypothesis tests (for impersonation detection) as well as a number of maximum-likelihood (ML) hypothesis tests (for transmitter identification when no impersonation is detected). We provide closed-form expressions for the error probabilities (i.e., the performance) of most of the hypothesis tests. We then consider the case of a UWA with colored noise and frequency-dependent pathloss, and derive a ML distance estimator as well as the corresponding Cramer-Rao bound. We then invoke the proposed two-step, impersonation detection framework by utilizing distance as the sole feature. Finally, we provide detailed simulation results for both AWGN UWA channel and the UWA channel with colored noise. Simulation results verify that the proposed scheme is indeed effective for a UWA channel with the colored noise and frequency-dependent pathloss. © 2013 IEEE.

AB - This paper considers a line-of-sight underwater acoustic (UWA) sensor network consisting of M underwater sensor nodes randomly deployed according to uniform distribution within a vertical half-disc (the so-called trusted zone). The sensor nodes report their sensed data to a sink node on water surface on a shared UWA reporting channel in a time-division multiple-access fashion, while an active-yet-invisible adversary (so-called Eve) is present in the close vicinity who aims to inject malicious data into the system by impersonating some Alice node. To this end, this paper first considers an additive white Gaussian noise (AWGN) UWA channel, and proposes a novel, multiple-feature-based, two-step method at the sink node to thwart the potential impersonation attack by Eve. Specifically, the sink node exploits the noisy estimates of the distance, the angle of arrival, and the location of the transmit node as device fingerprints to carry out a number of binary hypothesis tests (for impersonation detection) as well as a number of maximum-likelihood (ML) hypothesis tests (for transmitter identification when no impersonation is detected). We provide closed-form expressions for the error probabilities (i.e., the performance) of most of the hypothesis tests. We then consider the case of a UWA with colored noise and frequency-dependent pathloss, and derive a ML distance estimator as well as the corresponding Cramer-Rao bound. We then invoke the proposed two-step, impersonation detection framework by utilizing distance as the sole feature. Finally, we provide detailed simulation results for both AWGN UWA channel and the UWA channel with colored noise. Simulation results verify that the proposed scheme is indeed effective for a UWA channel with the colored noise and frequency-dependent pathloss. © 2013 IEEE.

KW - hypothesis testing

KW - Impersonation detection

KW - maximum likelihood detection & estimation and Cramer-Rao bound

KW - physical layer authentication

KW - underwater acoustic sensor networks

KW - Acoustic devices

KW - Acoustic noise

KW - Cramer-Rao bounds

KW - Cryptography

KW - Frequency estimation

KW - Gaussian noise (electronic)

KW - Jamming

KW - Maximum likelihood estimation

KW - Sensor networks

KW - Sensor nodes

KW - Time division multiple access

KW - White noise

KW - Additive White Gaussian noise

KW - AWGN channel

KW - Binary hypothesis tests

KW - Colored noise

KW - Maximum likelihood hypothesis

KW - Time division multiple accesses (TDMA)

KW - Transmitter identification

KW - Underwater acoustic sensor networks

KW - Underwater acoustics

U2 - 10.1109/ACCESS.2018.2863945

DO - 10.1109/ACCESS.2018.2863945

M3 - Journal article

VL - 6

SP - 44459

EP - 44472

JO - IEEE Access

JF - IEEE Access

SN - 2169-3536

ER -