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Abstract Coping with the problem of malicious third‐party vendors implanting Hardware Trojan (HT) in the circuit design stage, this paper proposes a hybrid‐mode gate‐level hardware Trojan detection platform based on the XGBoost algorithm. This detection platform is composed of multi‐level HT localization and circuit structure based HT detection. Each wire of the circuit is regarded as a node in multi‐level HT localization, and static characteristics of nodes are analysed, combining with dynamic detection to locate HT. The network structure features of the circuit are extracted in modular HT structure detection, aiming to identify HT accurately and rapidly. The hybrid‐mode HT detection platform can efficiently meet various detection requirements, such as HT localization or rapid and accurate HT detection. The experiment results on Trust‐Hub benchmark show that the multi‐level localization can achieve 94.0% location accuracy, and the modular HT structure detection accuracy can achieve 100%. The modular HT structure detection is about four times as fast as the multi‐level HT localization on feature extraction. Therefore, multi‐level localization and modular HT structure detection can be respectively or cooperatively applied for specific HT detection issues, which proves that the proposed hybrid‐mode gate‐level HT detection scheme is practical and effective.

Abstract Modern electronic systems usually use third‐party IP cores to build basic blocks. However, there may be Hardware Trojans (HTs) in IP cores, which will cause critical security problem. There are already many HT detection methods which claim to detect all publicly available HT benchmarks. But these methods can still be defeated by designing novel HTs. In this article, a method called Blinding HT is proposed, which camouflages itself as a normal circuit and is difficult to be triggered. The Blinding HT hides input signals of HT modules by tracing across multiple sequential levels. This method increases the influence of HT trigger inputs on output signals, so that trigger inputs are not be identified as redundant inputs. In this way, this approach can defeat the detection methods which identify weakly affecting trigger inputs and redundant trigger inputs across multiple sequential levels. As shown in the experimental results, the proposed HTs are hardly detected even by the novel HT detection approach based on machine learning algorithm. These HTs have small footprints on the design in terms of area and power to resist the side‐channel effect analysis. The proposed HT has stealthiness, general applicability and imperceptibility.

Patients with advanced cancers who either do not experience initial response to or progress while on immune checkpoint inhibitors (ICIs) receive salvage radiotherapy to reduce tumor burden and tumor-related symptoms. Occasionally, some patients experience substantial global tumor regression with a rebound of cytotoxic CD8+ T cells. We have termed the rebound of cytotoxic CD8+ T cells in response to salvage therapy as T cell resilience and examined the underlying mechanisms of resilience. Resilient T cells are enriched for CX3CR1+ CD8+ T cells with low mitochondrial membrane potential, accumulate less reactive oxygen species (ROS), and express more malic enzyme 1 (ME1). ME1 overexpression enhanced the cytotoxicity and expansion of effector CD8+ T cells partially via the type I interferon pathway. ME1 also increased mitochondrial respiration while maintaining the redox state balance. ME1 increased the cytotoxicity of peripheral lymphocytes from patients with advanced cancers. Thus, preserved resilient T cells in patients rebound after salvage therapy and ME1 enhances their resiliency. [ABSTRACT FROM AUTHOR]