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

Author

Listed:
  • Somdip Dey

    (School of Computer Science and Electronic Engineering (CSEE), University of Essex, Colchester CO4 3SQ, UK
    Nosh Technologies, Colchester CO4 3SL, 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)

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.

Suggested Citation

  • Somdip Dey & Amit Kumar Singh & Klaus McDonald-Maier, 2021. "ThermalAttackNet: Are CNNs Making It Easy to Perform Temperature Side-Channel Attack in Mobile Edge Devices?," Future Internet, MDPI, vol. 13(6), pages 1-10, May.
    • Handle: RePEc:gam:jftint:v:13:y:2021:i:6:p:146-:d:566177
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