Cluster-BPI: Efficient Fine-Grain Blind Power Identification for Defending against Hardware Thermal Trojans in Multicore SoCs
Mohamed R. Elshamy, Mehdi Elahi, Ahmad Patooghy, Abdel‐Hameed A. Badawy · 2024
Modern multicore System-on-Chips (SoCs) include hardware monitoring mechanisms to measure total power consumption, but these aggregate measurements are insufficient for fine-grained thermal and power management. This paper introduces an improved Clustering Blind Power Identification (ICBPI), an approach to improve the sensitivity and robustness of the Blind Power Identification (BPI) approach, which identifies the power consumption of different cores and the thermal model of an SoC using only thermal sensor measurements and the total power consumption. The proposed approach enhances BPI’s initialization step (specifically the non-negative matrix factorization, which is crucial for BPI accuracy) by incorporating density-based spatial clustering of of noise applications (DBSCAN). This is done to maximize the physical relationship between the temperature and power consumption, ensuring more accurate power estimates. Our simulations demonstrate two tasks to validate the proposed approach. The first evaluates the power accuracy per core on four different multicores, including a heterogeneous processor, showing that ICBPI significantly improves accuracy without overheads. For example, in a four-core SoC, error rates are reduced by 77.56% compared to vanilla BPI and by 68.44% compared to the state-of-the-art approach called BPISS. The second task focuses on enhancing the precision and robustness of the detection and localization of malicious thermal sensor attacks in the heterogeneous processor, demonstrating that ICBPI is capable of enhancing security of multicore SoCs.