VARI-CHECK: Authentication of COTS Devices using ML-Based Variability Characterization

Counterfeit devices are a growing threat to trust in electronic devices, involving the production and distribution of fake components or reselling out-of-spec ones.These pose risks like compromised functionality, security vulnerabilities, and economic losses when purchasing Commercial Off-The-Shelf (COTS) devices like FPGA and microcontrollers.To address this, we introduce VARI-CHECK, a low-overhead method for authenticating COTS devices.VARI-CHECK leverages inherent fabrication process variations to create digital signatures for device authentication.We validate this approach through experiments on FPGA devices, training a machine learning model to identify non-original devices.We test it on 100 oscillating/bistable circuit elements across 40 FPGA devices, achieving 80.4% success with Isolation Forest and 81.25% with One Class SVM.