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ThermalAttackNet: Are CNNs Making It Easy To Perform Temperature Side-Channel Attack In Mobile Edge Devices?

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posted on 23.07.2020 by Somdip Dey, Amit Kumar, Klaus D. Mcdonald-Maier

Side-channel attacks remain a challenge to information flow control and security in mobile edge devices till this date. One such important security flaw could be exploited through temperature side-channel attacks, where heat dissipation and propagation from the processing cores are observed over time in order to deduce security flaws. In this brief, we study how computer vision based convolutional neural networks (CNNs) could be used to exploit temperature (thermal) side-channel attack on different Linux governors in mobile edge device utilizing multi- processor system-on-chip (MPSoC). We also designed a power- and memory-efficient CNN model that is capable of performing thermal side-channel attack on the MPSoC and can be used by industry practitioners and academics as a benchmark to design methodologies to secure against such an attack in MPSoC.

Funding

National Centre for Nuclear Robotics (NCNR)

Engineering and Physical Sciences Research Council

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Robust remote sensing for multi-modal characterisation in nuclear and other extreme environments

Engineering and Physical Sciences Research Council

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History

Email Address of Submitting Author

somdip.dey@essex.ac.uk

ORCID of Submitting Author

0000-0001-6161-4637

Submitting Author's Institution

University of Essex

Submitting Author's Country

United Kingdom

Licence

Exports