Impersonation Detection in AWGN-limited Underwater Acoustic Sensor Networks
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This work addresses the problem of impersonation detection in an underwater acoustic sensor network (UWASN). We consider a UWASN 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 an additive white gaussian noise (AWGN) reporting channel in a time-division multiple-access (TDMA) fashion. The ongoing communication on the shared reporting channel is at risk of potential impersonation attack by an active-yet-invisible adversary (so-called Eve) present in the close vicinity, who aims to inject malicious data into the system. To this end, this work proposes a novel, two-step method at the sink node to thwart the potential impersonation attack by Eve. We assume that the sink node is equipped with a uniform linear array of hydrophones; and therefore, the estimates of the distance, angle of arrival, and the location of the transmit node are available at the sink node. The sink node exploits these measurements as device fingerprints to carry out a number of binary hypothesis tests (for impersonation attack detection) as well as a number of maximum likelihood 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. Furthermore, extensive simulation results (for various scenarios of Eve's location) are provided, which attest to the efficacy of the proposed scheme.
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