iThermTroj: Exploiting Intermittent Thermal Trojans in Multi-Processor System-on-Chips

TL;DR

This paper introduces iThermTroj, a novel type of intermittent thermal Trojan that evades detection by exploiting thermal information in SoCs, and proposes machine learning-based detection methods that significantly improve detection rates and resolution.

Contribution

The paper presents a new class of thermal Trojans, iThermTroj, and develops tiny ML classifiers for effective run-time detection, enhancing security against evasive thermal attacks.

Findings

iThermTroj can bypass threshold-based detection methods.

ML classifiers improve detection rates by up to 29.4%.

Detection resolution is increased to 0.8°C for temperature manipulations.

Abstract

Thermal Trojan attacks present a pressing concern for the security and reliability of System-on-Chips (SoCs), especially in mobile applications. The situation becomes more complicated when such attacks are more evasive and operate sporadically to stay hidden from detection mechanisms. In this paper, we introduce Intermittent Thermal Trojans (iThermTroj) that exploit the chips' thermal information in a random time-triggered manner. According to our experiments, iThermTroj attack can easily bypass available threshold-based thermal Trojan detection solutions. We investigate SoC vulnerabilities to variations of iThermTroj through an in-depth analysis of Trojan activation and duration scenarios. We also propose a set of tiny Machine Learning classifiers for run-time anomaly detection to protect SoCs against such intermittent thermal Trojan attacks. Compared to existing methods, our approach improves the attack detection rate by 29.4\%, 17.2\%, and 14.3\% in scenarios where iThermTroj manipulates up to 80\%, 60\%, and 40\% of SoC's thermal data, respectively. Additionally, our method increases the full protection resolution to 0.8 degrees Celsius, meaning that any temperature manipulations exceeding $\pm 0.8$ degrees will be detected with 100\% accuracy.