Improving the Ability of Thermal Radiation Based Hardware Trojan Detection
Ting Su, Yaohua Wang, Shi Xu, Lusi Zhang, Simin Feng, Jialong Song, Yiming Liu, Yongkang Tang, Yang Zhang, Shaoqing Li, Yang Guo, Hengzhu Liu
摘要
Hardware Trojans (HTs) pose a significant and growing threat to the field of hardware security. Several side-channel techniques, including power and electromagnetic radiation (EMR), have been proposed for HT detection, constrained by reliance on the golden chip or test vectors. In response, researchers advocate for the use of thermal radiation (TR) to identify HTs. However, existing TR-based methods are designed for the ideal HT that can fully occupy at least one pixel on the thermal radiation map (TRM). In reality, HTs may occupy multiple pixels, substantially diminishing occupancy in each pixel, thereby reducing the accuracy of existing detection methods. This challenge is exacerbated by the noise caused by the thermal camera. To this end, this paper introduces a countermeasure named noise based pixel occupation enhancement (NICE), aiming to improve the ability of TR-based HT detection. The key insight of NICE is that noise can vary the pixel occupation of HTs while disrupting HT detection. Consequently, the noise can be exploited to statistically find out the largest pixel occupation among the variations, thereby enhancing HT detection accuracy. Experimental results on a 0.13 µm Digital Signal Processing (DSP) show that the detection rate of NICE exceeds the existing TR-based method by more than 47%, reaching 91.81%, while maintaining a false alarm rate of less than 9%. Both metrics of NICE are comparable to the existing power-based and EMR-based methods, eliminating the need for the golden chip and test vectors.
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它引用的顶会 Paper5
- A2: Analog Malicious HardwareKaiyuan Yang, Matthew Hicks, Qing Dong, Todd M. Austin 等S&P 2016 · 被引用 242 次
- ICAS: an Extensible Framework for Estimating the Susceptibility of IC Layouts to Additive TrojansTimothy Trippel, Kang G. Shin, Kevin B. Bush, Matthew HicksS&P 2020 · 被引用 35 次
- ATTRITION: Attacking Static Hardware Trojan Detection Techniques Using Reinforcement LearningVasudev Gohil, Hao Guo, Satwik Patnaik, Jeyavijayan RajendranCCS 2022 · 被引用 34 次
- Jinn: Hijacking Safe Programs with TrojansKomail Dharsee, John CriswellUSENIX Security 2023
- Red Team vs. Blue Team: A Real-World Hardware Trojan Detection Case Study Across Four Modern CMOS Technology GenerationsEndres Puschner, Thorben Moos, Steffen Becker, Christian Kison 等S&P 2023
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