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1. Hardware Trojan Testing with Hierarchical Trojan Types Under Cognitive Biases

   Nan, Satyaki ; Brahma, S. ( March 2024 , Annual Conference on Information Sciences and Systems)
2. Intelligent Electromagnetic Sensors for Non-Invasive Trojan Detection

   https://doi.org/10.3390/s21248288  

   Chen, Ethan ; Kan, John ; Yang, Bo-Yuan ; Zhu, Jimmy ; Chen, Vanessa ( December 2021 , Sensors)

   Rapid growth of sensors and the Internet of Things is transforming society, the economy and the quality of life. Many devices at the extreme edge collect and transmit sensitive information wirelessly for remote computing. The device behavior can be monitored through side-channel emissions, including power consumption and electromagnetic (EM) emissions. This study presents a holistic self-testing approach incorporating nanoscale EM sensing devices and an energy-efficient learning module to detect security threats and malicious attacks directly at the front-end sensors. The built-in threat detection approach using the intelligent EM sensors distributed on the power lines is developed to detect abnormal data activities without degrading the performance while achieving good energy efficiency. The minimal usage of energy and space can allow the energy-constrained wireless devices to have an on-chip detection system to predict malicious attacks rapidly in the front line. 

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3. Photometric Survey of Neptune's Trojan Asteroids. I. The Color Distribution

   https://doi.org/10.3847/PSJ/ace528  

   Markwardt, Larissa ; Wen Lin 林, Hsing 省文 ; Gerdes, David ; Adams, Fred C. ( August 2023 , The Planetary Science Journal)

   In 2018, Jewitt identified the “The Trojan Color Conundrum,” namely that Neptune's Trojan asteroids (NTs) had no ultrared members, unlike the the nearby Kuiper Belt. Since then, numerous ultrared NTs have been discovered, seemingly resolving this conundrum. However, it is still unclear whether or not the Kuiper Belt has a color distribution consistent with the NT population, as would be expected if it were the source population. In this work, we present a new photometric survey of 15 out of 31 NTs. We utilized the Sloan$g\prime r\prime i\prime z\prime$filters on the IMACS f/4 instrument, which is mounted on the 6.5 m Baade telescope. In this survey, we identify four NTs as being ultrared using a principal component analysis. This result brings the ratio of red to ultrared NTs to 7.75:1, more consistent with the corresponding trans-Neptunian object ratio of 4–11:1. We also identify three targets as being blue (nearly solar) in color. Such objects may be C-type surfaces, but we see more of these blue NTs than has been observed in the Kuiper Belt. Finally, we show that there are hints of a color-absolute magnitude (H) correlation, with larger H (smaller sized, lower albedo) tending to be more red, but more data are needed to confirm this result. The origin of such a correlation remains an open question that will be addressed by future observations of the surface composition of these targets and their rotational properties.

    

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4. Hardware Trojan Attacks: Threat Analysis and Countermeasures

   https://doi.org/10.1109/JPROC.2014.2334493  

   Bhunia, Swarup ; Hsiao, Michael S. ; Banga, Mainak ; Narasimhan, Seetharam ( August 2014 , Proceedings of the IEEE)
5. Risk-Aware and Explainable Framework for Ensuring Guaranteed Coverage in Evolving Hardware Trojan Detection

   https://doi.org/10.1109/ICCAD57390.2023.10323655  

   Vishwakarma, Rahul ; Rezaei, Amin ( October 2023 , Proceedings of International Conference on Computer Aided Design (ICCAD))

   As the semiconductor industry has shifted to a fabless paradigm, the risk of hardware Trojans being inserted at various stages of production has also increased. Recently, there has been a growing trend toward the use of machine learning solutions to detect hardware Trojans more effectively, with a focus on the accuracy of the model as an evaluation metric. However, in a high-risk and sensitive domain, we cannot accept even a small misclassification. Additionally, it is unrealistic to expect an ideal model, especially when Trojans evolve over time. Therefore, we need metrics to assess the trustworthiness of detected Trojans and a mechanism to simulate unseen ones. In this paper, we generate evolving hardware Trojans using our proposed novel conformalized generative adversarial networks and offer an efficient approach to detecting them based on a non-invasive algorithm-agnostic statistical inference framework that leverages the Mondrian conformal predictor. The method acts like a wrapper over any of the machine learning models and produces set predictions along with uncertainty quantification for each new detected Trojan for more robust decision-making. In the case of a NULL set, a novel method to reject the decision by providing a calibrated explainability is discussed. The proposed approach has been validated on both synthetic and real chip-level benchmarks and proven to pave the way for researchers looking to find informed machine learning solutions to hardware security problems. 

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