Attack mitigation of hardware trojans for thermal sensing via micro-ring resonator in optical NoCs

• Main Authors: Zhou, Jun, Li, Mengquan, Guo, Pengxing, Liu, Weichen
• Other Authors: School of Computer Science and Engineering
• Format: Article
• Language: English
• Published: 2021
• Subjects: Engineering::Computer science and engineering::Hardware::Input/output and data communications; Hardware Security; Thermal Sensing; Network-on-chip
• Online Access: https://hdl.handle.net/10356/152430
• Institution: Nanyang Technological University

As an emerging role in new-generation on-chip communication, optical networks-on-chip (ONoCs) provide ultra-high bandwidth, low latency and low power dissipation for data transfers. However, the thermo-optic effects of the photonic devices have a great impact on the operating performance and reliability of ONoCs, where the thermal-aware control with accurate measurements, e.g., thermal sensing, is typically applied to alleviate it. Besides, the temperature-sensitive ONoCs are prone to be attacked by the hardware Trojans (HTs) covertly embedded in the counterfeit integrated circuits (ICs) from the malicious third-party vendors, leading to performance degradation, denial-of-service (DoS), or even permanent damages. In this paper, we focus on the tampering and snooping attacks during the thermal sensing via micro-ring resonator (MR) in ONoCs. Based on the provided work flow and attack model, a new structure of the anti-HT module is proposed to verify and protect the obtained data from the thermal sensor for attacks in its optical sampling and electronic transmission processes. In addition, we present the detection scheme based on the spiking neural networks (SNNs) to implement an accurate classification of the network security statuses for further high-level control. Evaluation results indicate that, with less than 1% extra area of a tile, our approach can significantly enhance the hardware security of thermal sensing for ONoC with trivial costs of up to 8.73%, 5.32% and 6.14% in average latency, execution time and energy consumption, respectively.