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No Seeing is Also Believing: Electromagnetic-Emission-Based Application Guessing Attacks via Smartphones | IEEE Journals & Magazine | IEEE Xplore

No Seeing is Also Believing: Electromagnetic-Emission-Based Application Guessing Attacks via Smartphones


Abstract:

Mobile devices have emerged as the most popular platforms to access information. However, they have also become a major concern of privacy violation and previous research...Show More

Abstract:

Mobile devices have emerged as the most popular platforms to access information. However, they have also become a major concern of privacy violation and previous researches have demonstrated various approaches to infer user privacy based on mobile devices. In this paper, we study the electromagnetic (EM) emission of a laptop that could be harvested by a commercial-off-the-shelf (COTS) mobile device, e.g., a smartphone. We propose MagAttack, which exploits the electromagnetic side channel of a laptop to guess user activities, i.e., application launching and application operation. The key insight of MagAttack is that applications are discrepant in essence due to the different compositions of instructions, which can be reflected on the CPU power consumption, and thus the corresponding EM emissions. MagAttack is challenging since that EM signals are noisy due to the dynamics of applications and the limited sampling rate of the built-in magnetometers in COTS mobile devices. We overcome these challenges and convert noisy coarse-grained EM signals to robust fine-grained features. We implement MagAttack on both an iOS and an Android smartphone without any hardware modification, and evaluate its performance with 30 popular applications, 30 YouTube videos, and 50 top websites in China. The results demonstrate that MagAttack can recognize aforementioned 30 applications with an average accuracy of 98.6 percent, and identify which video out of the 30 candidates being played with an average accuracy of 97.5 percent and visiting which website among the 50 candidates with an average accuracy of 90.4 percent.
Published in: IEEE Transactions on Mobile Computing ( Volume: 22, Issue: 2, 01 February 2023)
Page(s): 1095 - 1109
Date of Publication: 24 June 2021

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