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

• Main Authors: Somdip Dey, Amit Kumar Singh, Klaus McDonald-Maier
• Format: Article
• Language: English
• Published: MDPI AG 2021-05-01
• Series: Future Internet
• Subjects: multiprocessor system-on-chip (MPSoC); thermal behavior; temperature side-channel attack; security; machine learning; convolutional neural network (CNN)
• Online Access: https://www.mdpi.com/1999-5903/13/6/146

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.