Classification and Novelty Detection of Tampered ICs Using ResCav

Abstract

AbstractThis paper investigates the capabilities of the resonant cavity system (ResCav) for detecting tampered integrated circuits (ICs) within supply chains. Prior research showcased ResCav’s ability to discern minor circuit variations, this study focuses on enhancing supervised classification results and introduces a one-class support vector machine (SVM) approach with a modified radial basis function kernel for novelty detection. Through finer hyperparameter tuning, the system achieves improved classification accuracy, demonstrating its potential to identify nuanced alterations with even higher precision and recall rates. Additionally, the application of a one-class SVM enables the detection of tampered ICs without reliance on labeled datasets, expanding utility in scenarios where access is limited to golden ICs. These advancements in ResCav’s capabilities signify progress in failure prevention methodologies, offering an efficient and non-destructive solution crucial for safeguarding against counterfeit and non-conforming components infiltrating critical supply chains.