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