Impersonation Detection in Line-of-Sight Underwater Acoustic Sensor Networks

• Published in: IEEE access Vol. 6; pp. 44459 - 44472
• Main Authors: Aman, Waqas, Rahman, Muhammad Mahboob Ur, Qadir, Junaid, Pervaiz, Haris, Ni, Qiang
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Acoustic noise; Angle of arrival; AWGN channels; Colored noise; Cramer-Rao bounds; Cryptography; Hypotheses; hypothesis testing; Impersonation detection; Jamming; Line of sight; maximum likelihood detection & estimation and Cramer-Rao bound; Maximum likelihood estimation; Nodes; Noise; physical layer authentication; Random noise; Sensors; Time Division Multiple Access; underwater acoustic sensor networks; Underwater acoustics; Underwater detectors; Vertical distribution
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2018.2863945

This paper considers a line-of-sight underwater acoustic (UWA) sensor network consisting of <inline-formula> <tex-math notation="LaTeX">M </tex-math></inline-formula> 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.