Out-of-Band Power Side-Channel Detection for Semiconductor Supply Chain Integrity at Scale

• URL: http://arxiv.org/abs/2601.01054v1
• Date: Sat, 03 Jan 2026 03:14:40 GMT
• Title: Out-of-Band Power Side-Channel Detection for Semiconductor Supply Chain Integrity at Scale
• Authors: Rajiv Thummala, Katherine Winton, Luke Flores, Elizabeth Redmond, Gregory Falco,
• Abstract summary: Out-of-band screening of microcontrollers is a major gap in semiconductor supply chain security.<n>High-assurance techniques such as X-ray and destructive reverse engineering are accurate but slow and expensive.<n>We introduce a non-destructive screening method that uses power side-channel measurements and generative modeling to detect tampering.
• Score: 0.4567122178196832
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Universal Transformation of One-Class Classifiers for Unsupervised Anomaly Detection [51.73001988341294]
  Anomaly detection is typically formulated as a one-class classification problem.<n>We present a dataset folding method that transforms an arbitrary one-class classifier-based anomaly detector into a fully unsupervised method.
  arXiv  Detail & Related papers  (2026-02-13T16:54:12Z)
• Advancing Machine-Generated Text Detection from an Easy to Hard Supervision Perspective [108.30620357325559]
  Existing machine-generated text (MGT) detection methods implicitly assume labels as the "golden standard"<n>We propose an easy-to-hard enhancement framework to provide reliable supervision under such inexact conditions.
  arXiv  Detail & Related papers  (2025-11-02T15:59:31Z)
• CANTXSec: A Deterministic Intrusion Detection and Prevention System for CAN Bus Monitoring ECU Activations [53.036288487863786]
  We propose CANTXSec, the first deterministic Intrusion Detection and Prevention system based on physical ECU activations.<n>It detects and prevents classical attacks in the CAN bus, while detecting advanced attacks that have been less investigated in the literature.<n>We prove the effectiveness of our solution on a physical testbed, where we achieve 100% detection accuracy in both classes of attacks while preventing 100% of FIAs.
  arXiv  Detail & Related papers  (2025-05-14T13:37:07Z)
• Lie Detector: Unified Backdoor Detection via Cross-Examination Framework [68.45399098884364]
  We propose a unified backdoor detection framework in the semi-honest setting.<n>Our method achieves superior detection performance, improving accuracy by 5.4%, 1.6%, and 11.9% over SoTA baselines.<n> Notably, it is the first to effectively detect backdoors in multimodal large language models.
  arXiv  Detail & Related papers  (2025-03-21T06:12:06Z)
• Real-Time Multi-Modal Subcomponent-Level Measurements for Trustworthy System Monitoring and Malware Detection [20.93359969847573]
  Modern computers are complex systems with multiple interacting subcomponents.<n>We propose a "subcomponent-level" approach to collect side channel measurements.<n>By enabling real-time measurements from multiple subcomponents, the goal is to provide a deeper visibility into system operation.
  arXiv  Detail & Related papers  (2025-01-22T18:44:00Z)
• Unsupervised Continual Anomaly Detection with Contrastively-learned Prompt [80.43623986759691]
  We introduce a novel Unsupervised Continual Anomaly Detection framework called UCAD. The framework equips the UAD with continual learning capability through contrastively-learned prompts. We conduct comprehensive experiments and set the benchmark on unsupervised continual anomaly detection and segmentation.
  arXiv  Detail & Related papers  (2024-01-02T03:37:11Z)
• Countering detector manipulation attacks in quantum communication through detector self-testing [0.0]
  A vulnerability in single-photon detectors to blinding attacks has been one of the biggest concerns. We present a countermeasure based on self-testing of detectors to confirm their intended operation.
  arXiv  Detail & Related papers  (2022-04-13T03:36:54Z)
• Inter-Domain Fusion for Enhanced Intrusion Detection in Power Systems: An Evidence Theoretic and Meta-Heuristic Approach [0.0]
  False alerts due to/ compromised IDS in ICS networks can lead to severe economic and operational damage. This work presents an approach for reducing false alerts in CPS power systems by dealing with uncertainty without prior distribution of alerts.
  arXiv  Detail & Related papers  (2021-11-20T00:05:39Z)
• Assurance Monitoring of Learning Enabled Cyber-Physical Systems Using Inductive Conformal Prediction based on Distance Learning [2.66512000865131]
  We propose an approach for assurance monitoring of learning-enabled Cyber-Physical Systems. In order to allow real-time assurance monitoring, the approach employs distance learning to transform high-dimensional inputs into lower size embedding representations. We demonstrate the approach using three data sets of mobile robot following a wall, speaker recognition, and traffic sign recognition.
  arXiv  Detail & Related papers  (2021-10-07T00:21:45Z)
• Signal Processing and Machine Learning Techniques for Terahertz Sensing: An Overview [89.09270073549182]
  Terahertz (THz) signal generation and radiation methods are shaping the future of wireless systems. THz-specific signal processing techniques should complement this re-surged interest in THz sensing for efficient utilization of the THz band. We present an overview of these techniques, with an emphasis on signal pre-processing. We also address the effectiveness of deep learning techniques by exploring their promising sensing capabilities at the THz band.
  arXiv  Detail & Related papers  (2021-04-09T01:38:34Z)
• Assurance Monitoring of Cyber-Physical Systems with Machine Learning Components [2.1320960069210484]
  We investigate how to use the conformal prediction framework for assurance monitoring of Cyber-Physical Systems. In order to handle high-dimensional inputs in real-time, we compute nonconformity scores using embedding representations of the learned models. By leveraging conformal prediction, the approach provides well-calibrated confidence and can allow monitoring that ensures a bounded small error rate.
  arXiv  Detail & Related papers  (2020-01-14T19:34:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.