Dynamic Thermal Management (DTM) systems are critical to the reliable operation of Multiprocessor System-on-Chips (MPSoCs), yet remain vulnerable to sophisticated thermal manipulation attacks. These attacks, executed through hardware trojans or privilege escalation, can compromise the integrity of thermal sensors, causing performance degradation, accelerated aging, and catastrophic hardware failure by disabling thermal throttling mechanisms. Existing countermeasures rely on reactive detection methods and conventional machine learning models that fail to capture the complex physics governing thermal systems, including thermal coupling across cores and power-frequency interdependencies, making them ineffective against multi-stage attacks that exploit DTM decision-making logic. This work presents a novel transformer-based defense framework that leverages self-attention mechanisms to model rich, system-wide feature interactions for detecting adversarial thermal manipulations in real time. The proposed hybrid architecture integrates an adaptive pre-filtering with dynamic thresholding to achieve an 83x throughput improvement (22,798 samples/second versus 274.53 samples/second for transformer-only baseline) and nearly 50% lower GPU utilization, enabling deployment on resource-constrained embedded platforms. Comprehensive on-device validation on the NVIDIA Jetson AGX Orin board demonstrates substantial thermal regulation improvements, reducing average peak temperatures from 103 °C to 98.5 °C while maintaining a model active ratio of only 2.73%. The framework incorporates an adaptive defense system with load-dependent dynamic thresholding that achieves high F1-scores in detecting the thermal attacks discussed in the literature (0.75 to 0.9). This work bridges the critical gap between simulation-based security research and practical embedded system deployment, establishing a new paradigm for lightweight, attention-based anomaly detection in thermally constrained environments.
Elahi et al. (Fri,) studied this question.