摘要：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.
关键词：multiprocessor system-on-chip (MPSoC); thermal behavior; temperature side-channel attack; security; machine learning; convolutional neural network (CNN); deep learning; energy efficiency; memory efficiency multiprocessor system-on-chip (MPSoC) ; thermal behavior ; temperature side-channel attack ; security ; machine learning ; convolutional neural network (CNN) ; deep learning ; energy efficiency ; memory efficiency
