PUF Tester
Hardware security testing toolkit for measuring unique device fingerprints and reliability under real test conditions.
Overview
PUF Tester evaluates how consistently and uniquely hardware devices can identify themselves, which is key for trusted systems. What makes it impressive is its focus on measurable evidence: it automates data collection and quantifies reliability and uniqueness. This turns security claims into concrete metrics teams can reason about.
Demo
No live demo available for this project. See the repo for setup and run instructions.
Description
responses.csv; the others compute reliability and uniqueness from that file for security and fingerprinting. Demonstrates hardware–software interface (SPI, PyFTDI), protocol design, and metric computation for device characterization.automated_script.py Uses PyFTDI’s
SpiController (e.g. ftdi://ftdi:2232h/1, 30 MHz SPI, mode 0). The protocol uses the lower 4 bits for command (START, SET_RUNS, SET_DELTA_HIGH, SET_DELTA_REST) and the upper 4 bits for data. configure_timing and configure_runs set timing and run count (1–15); start_run sends START and reads 16×runs bytes. main() configures timing and runs, triggers a run, and writes the response rows to responses.csv.SPI_Interface.py Defines
hamming(t1, t2) (element-wise inequality count). calc_reliability averages Hamming distance between a reference response and repeated responses (lower is more reliable). calc_uniqueness averages pairwise Hamming distance across different responses (higher is more unique). golden_response picks the most frequent response as the reference.testing.py Reads
responses.csv, sets response_ref = golden_response(responses), then calls calc_reliability and calc_uniqueness and prints the metrics (e.g. for validation or characterization reports).Tech & tools
- Hardware
- PUF
- Security
- Characterization
Highlights
- PUF characterization and measurement workflows
- Hardware-level testing and data collection
- Support for security and device fingerprinting use cases
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