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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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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 -