Strong PUF Challenge Screening for Stability
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Solution Overview
Problem
Strong Physical Unclonable Functions (PUFs) are unstable under environmental noise and transmission errors, leading to security risks in IoT applications, as existing solutions like error-correcting codes and temporal majority voting are not suitable for strong PUFs and increase area expenditure.
Innovation Solution
A challenge screening method using machine learning to identify and filter out unstable challenges, forming challenge-stability pairs, training a model to predict stable challenges, and using this model to screen challenges before inputting them to the strong PUF, thereby improving stability without increasing area expenditure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If error-correcting code (ECC) is added to improve stability, then stability of strong PUF is improved, but area expenditure increases
Solution Approach 1:
The patent extracts and removes unstable challenges from the challenge set through a screening process. By identifying and eliminating challenges that produce unstable responses, the system improves PUF stability without adding physical error correction circuits, thus avoiding area expenditure while maintaining reliability
Solution Approach 2:
The patent performs preliminary screening of challenges before they are used in authentication. By pre-identifying and removing unstable challenges through repeated testing and stability evaluation, the system prevents instability issues before they affect authentication, eliminating the need for post-processing error correction
2Reliability
If threshold c is decreased to ensure authentication success with unstable challenges, then authentication success rate is improved, but security risk increases
Solution Approach 1:
The patent performs preliminary identification and removal of unstable challenges before authentication. By pre-screening challenges and keeping only stable ones, the system ensures high authentication success rates without needing to lower the threshold, thereby maintaining security while improving reliability
3Measurement precision
If machine learning model is trained with large number of challenge-response pairs, then screening accuracy is improved, but training time and computational resources increase
Solution Approach 1:
The patent uses a sufficient but not excessive number of challenge-response pairs for training (e.g., 10,000 to 100,000 pairs). This partial action approach achieves high screening accuracy without requiring exhaustive training data, balancing model performance with training efficiency and resource consumption
Data Source
AI summary
The invention discloses a challenge screening method for improving the stability of a strong Physical Unclonable Function (PUF). If the stability of the group of challenges (GOCs) is quantized by 1 or 0, the GOCs are defined as stable challenges or unstable challenges respectively; the GOCs and the stability of corresponding responses are defined as a challenge-stability pair (CSP); a machining learning training dataset is constructed through CSPs for obtaining a screening model, to associate the challenge stability with the screening model; during actual application of the strong PUF, the challenge stability of a strong PUF is determined through the screening model, and stable challenges of the strong PUF are screened out to form a stable challenge set, which is input into the strong PUF to extract identity information. The challenge screening method can effectively improve the stability of various strong PUFs and has better effect with external disturbance increasing.