AquaSonic: Acoustic Manipulation of Underwater Data Center Operations and Resource Management

Jennifer Sheldon; Weidong Zhu; Adnan Abdullah; Sri Hrushikesh Varma Bhupathiraju; Takeshi Sugawara; Kevin R. B. Butler; Md Jahidul Islam; Sara Rampazzi

arXiv · 2404.11815

AquaSonic: Acoustic Manipulation of Underwater Data Center Operations and Resource Management

Jennifer Sheldon·Weidong Zhu·Adnan Abdullah·Sri Hrushikesh Varma Bhupathiraju·Takeshi Sugawara·Kevin R. B. Butler·Md Jahidul Islam·Sara Rampazzi

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Abstract

Underwater datacenters (UDCs) hold promise as next-generation data storage due to their energy efficiency and environmental sustainability benefits. While the natural cooling properties of water save power, the isolated aquatic environment and long-range sound propagation in water create unique vulnerabilities which differ from those of on-land data centers. Our research discovers the unique vulnerabilities of fault-tolerant storage devices, resource allocation software, and distributed file systems to acoustic injection attacks in UDCs. With a realistic testbed approximating UDC server operations, we empirically characterize the capabilities of acoustic injection underwater and find that an attacker can reduce fault-tolerant RAID 5 storage system throughput by 17% up to 100%. Our closed-water analyses reveal that attackers can (i) cause unresponsiveness and automatic node removal in a distributed filesystem with only 2.4 minutes of sustained acoustic injection, (ii) induce a distributed database's latency to increase by up to 92.7% to reduce system reliability, and (iii) induce load-balance managers to redirect up to 74% of resources to a target server to cause overload or force resource colocation. Furthermore, we perform open-water experiments in a lake and find that an attacker can cause controlled throughput degradation at a maximum allowable distance of 6.35 m using a commercial speaker. We also investigate and discuss the effectiveness of standard defenses against acoustic injection attacks. Finally, we formulate a novel machine learning-based detection system that reaches 0% False Positive Rate and 98.2% True Positive Rate trained on our dataset of profiled hard disk drives under 30-second FIO benchmark execution. With this work, we aim to help manufacturers proactively protect UDCs against acoustic injection attacks and ensure the security of subsea computing infrastructures.

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Jennifer Sheldon, Weidong Zhu, Adnan Abdullah, Sri Hrushikesh Varma Bhupathiraju, Takeshi Sugawara, Kevin R. B. Butler, Md Jahidul Islam, Sara Rampazzi. 2024-05-08. AquaSonic: Acoustic Manipulation of Underwater Data Center Operations and Resource Management. https://arxiv.org/abs/2404.11815

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