Out-of-band screening of microcontrollers is a major gap in semiconductor supply chain security. High-assurance techniques such as X-ray and destructive reverse engineering are accurate but slow and expensive, hindering comprehensive detection for hardware Trojans or firmware tampering. Consequently, there has been increased interest in applying machine learning techniques to automate forensic examination, enabling rapid, large-scale inspection of components without manual oversight. We introduce a non-destructive screening method that uses power side-channel measurements and generative modeling to detect tampering in commodity microcontrollers without trusted hardware. As a proof-of-concept, differential power analysis (DPA) traces are collected from the ChipWhisperer and a generative adversarial network (GAN) is trained only on benign measurements to learn nominal power behavior. The trained discriminator then serves as a one-class anomaly detector. We report detection performance on multiple tampering scenarios and discuss how this technique can serve as an intermediate screening tier between basic functional tests and high-cost forensic analysis. The proposed method is evaluated in the context of semiconductor supply chain practice and policy to assess its suitability as an intermediate assurance mechanism.

翻译：微控制器的带外筛查是半导体供应链安全中的一个主要薄弱环节。X射线检测和破坏性逆向工程等高可信度技术虽然精确，但速度慢且成本高昂，阻碍了对硬件木马或固件篡改的全面检测。因此，应用机器学习技术实现取证检查自动化日益受到关注，该技术能够在无需人工监督的情况下对元器件进行快速、大规模检测。本文提出一种非破坏性筛查方法，该方法利用功率侧信道测量和生成式建模技术，在不依赖可信硬件的条件下检测商用微控制器的篡改行为。作为概念验证，我们通过ChipWhisperer采集差分功率分析（DPA）轨迹，并仅使用良性测量数据训练生成对抗网络（GAN）以学习标称功率行为。训练后的判别器可作为单类异常检测器使用。我们报告了该方法在多种篡改场景下的检测性能，并讨论了该技术如何作为基础功能测试与高成本取证分析之间的中间筛查层级。结合半导体供应链实践与政策背景，我们对所提方法作为中间可信度保障机制的适用性进行了评估。