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

Somdip Dey, Amit Kumar Singh and Klaus McDonald-Maier
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Somdip Dey: School of Computer Science and Electronic Engineering (CSEE), University of Essex, Colchester CO4 3SQ, UK
Amit Kumar Singh: School of Computer Science and Electronic Engineering (CSEE), University of Essex, Colchester CO4 3SQ, UK
Klaus McDonald-Maier: School of Computer Science and Electronic Engineering (CSEE), University of Essex, Colchester CO4 3SQ, UK

Future Internet, 2021, vol. 13, issue 6, 1-10

Abstract: 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 paper, 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.

Keywords: multiprocessor system-on-chip (MPSoC); thermal behavior; temperature side-channel attack; security; machine learning; convolutional neural network (CNN); deep learning; energy efficiency; memory efficiency (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2021
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