标题： iThermTroj：在多处理器片上系统中利用间歇性热特洛伊 显示英文标题

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

摘要： 热托洛伊攻击对片上系统（SoCs）的安全性和可靠性构成了紧迫的威胁，尤其是在移动应用中。 当此类攻击更加隐蔽且间歇性运行以躲避检测机制时，情况变得更加复杂。 在本文中，我们介绍了间歇性热托洛伊（iThermTroj），它以随机时间触发的方式利用芯片的热信息。 根据我们的实验，iThermTroj攻击可以轻松绕过现有的基于阈值的热托洛伊检测解决方案。 我们通过深入分析木马激活和持续时间场景，研究了SoC对iThermTroj变化的漏洞。 我们还提出了一组小型机器学习分类器用于运行时异常检测，以保护SoCs免受这种间歇性热托洛伊攻击。 与现有方法相比，我们的方法在iThermTroj操纵多达80%、60%和40%的SoC热数据的情况下，分别将攻击检测率提高了29.4%、17.2%和14.3%。 此外，我们的方法将完整的保护分辨率提高到0.8摄氏度，这意味着任何超过 $\pm 0.8$ 摄氏度的温度操作都将被100%准确地检测到。 显示英文摘要

摘要： 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.