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.
Highlights
Internet 2021, 13, 146. https://Recently, mobile devices have become an integral part of daily life
Most of these mobile devices come equipped with heterogeneous multi-processors systems-on-chip (MPSoC), which consists of multiple heterogeneous processors on a single chip, capable of processing different types of applications to cater for performance and energy-efficiency of the executing applications
Given the fact that ondemand and performance governors are more vulnerable to attacks similar to the proposed one, some form of software/hardware mechanisms should be employed in mobile edge devices employing such governors such that either the peak temperature achieved during the encryption process could be masked or such that the peak temperature does not increase during the encryption process
Summary
Mobile devices have become an integral part of daily life. These mobile devices are utilized to run different types of applications, including video calling, web browsing, gaming, navigation; energy-efficient processing on these battery-empowered mobile devices is of utmost importance [1,2]. A lot less documented studies are performed in temperature (thermal)based side channel attacks [4,5,6] in mobile edge devices. Most of these mobile devices come equipped with heterogeneous multi-processors systems-on-chip (MPSoC), which consists of multiple heterogeneous processors on a single chip, capable of processing different types of applications to cater for performance and energy-efficiency of the executing applications. Design and implementation of a power- and memory-efficient CNN model, ThermalAttackNet, to perform thermal side-channel attack on a real consumer mobile device.
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