Active Localization of Close-range Adversarial Acoustic Sources for Underwater Data Center Surveillance

TL;DR

This paper introduces a real-time, geometry-aware underwater surveillance framework that accurately localizes adversarial acoustic sources near data centers, enhancing security against acoustic attacks.

Contribution

It develops a novel LC-MAP scheme integrated with UKF for reliable 3D localization of acoustic threats in dynamic underwater environments.

Findings

Achieves sub-meter localization accuracy.

Over 90% success rate in threat detection.

Convergence times nearly half of baseline methods.

Abstract

Underwater data infrastructures offer natural cooling and enhanced physical security compared to terrestrial facilities, but are susceptible to acoustic injection attacks that can disrupt data integrity and availability. This work presents a comprehensive surveillance framework for localizing and tracking close-range adversarial acoustic sources targeting offshore infrastructures, particularly underwater data centers (UDCs). We propose a heterogeneous receiver configuration comprising a fixed hydrophone mounted on the facility and a mobile hydrophone deployed on a dedicated surveillance robot. While using enough arrays of static hydrophones covering large infrastructures is not feasible in practice, off-the-shelf approaches based on time difference of arrival (TDOA) and frequency difference of arrival (FDOA) filtering fail to generalize for this dynamic configuration. To address this, we formulate a Locus-Conditioned Maximum A-Posteriori (LC-MAP) scheme to generate acoustically informed and geometrically consistent priors, ensuring a physically plausible initial state for a joint TDOA-FDOA filtering. We integrate this into an unscented Kalman filtering (UKF) pipeline, which provides reliable convergence under nonlinearity and measurement noise. Extensive Monte Carlo analyses, Gazebo-based physics simulations, and field trials demonstrate that the proposed framework can reliably estimate the 3D position and velocity of an adversarial acoustic attack source in real time. It achieves sub-meter localization accuracy and over 90% success rates, with convergence times nearly halved compared to baseline methods. Overall, this study establishes a geometry-aware, real-time approach for acoustic threat localization, advancing autonomous surveillance capabilities of underwater infrastructures.