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OutletSpy: cross-outlet application inference via power factor correction signal

Published:28 June 2021Publication History

ABSTRACT

Trade secrets such as intellectual properties are the inherent values for firms. Although companies have exploited strict access management policies and isolated their networks from the public Internet, trade secrets are still vulnerable to side-channel attacks. Side-channels can reveal the computing processes of computers in forms of various physical signals such as light, electromagnetism, and even heat. Such side-channels can bypass the isolation mechanism and therefore bring about severe threats. However, existing side-channels can only perform well within a short-distance (e.g., less than 1 meter) due to the high attenuation of signals. In this paper, we seek to utilize the built-in power lines in a building and construct a power side-channel that enables remote, i.e., cross-outlet attack against trade secrets. To this end, we investigate the power factor correction (PFC) module inside the power supply units of commodity computers and find that the PFC signals observed from an outlet can precisely reveal the power consumption information of all the connected devices, even from the outlets in adjacent rooms. Based upon this insight, we design and implement OutletSpy, a power side-channel attack that can infer application launching from a remote outlet and therefore enjoys the stealthiness property. We validate and evaluate OutletSpy with a dataset under different background APPs, time variations and different locations. The experiment results show OutletSpy can infer the application launching with 98.25% accuracy.

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    • Published in

      cover image ACM Conferences
      WiSec '21: Proceedings of the 14th ACM Conference on Security and Privacy in Wireless and Mobile Networks
      June 2021
      412 pages
      ISBN:9781450383493
      DOI:10.1145/3448300

      Copyright © 2021 ACM

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      • Published: 28 June 2021

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      WiSec '21 Paper Acceptance Rate34of121submissions,28%Overall Acceptance Rate98of338submissions,29%

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