A Lightweight and High-Reliability Challenge Generation Strategy for APUF

LAN Guohao; ZHANG Hui; DUO Bin; WANG Zibin; ZHOU Rang; LI Dongfen

doi:10.11999/JEIT251073

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LAN Guohao, ZHANG Hui, DUO Bin, WANG Zibin, ZHOU Rang, LI Dongfen. A Lightweight and High-Reliability Challenge Generation Strategy for APUF[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251073

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LAN Guohao, ZHANG Hui, DUO Bin, WANG Zibin, ZHOU Rang, LI Dongfen. A Lightweight and High-Reliability Challenge Generation Strategy for APUF[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251073

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A Lightweight and High-Reliability Challenge Generation Strategy for APUF

doi: 10.11999/JEIT251073 cstr: 32379.14.JEIT251073

1.
School of Computer and Network Security, Chengdu University of Technology, Chengdu 610059, China
2.
Key Laboratory of Information System Security Technology, Beijing 100093, China

Funds: Higher Education Talent Training Quality and Teaching Reform Project (JG2420017, JG2430165)

Received Date: 2025-10-11
Accepted Date: 2026-03-18
Rev Recd Date: 2026-03-17

Available Online: 2026-04-06

Abstract

Abstract

Objective The Arbiter Physical Unclonable Function (APUF) is a lightweight security primitive widely used for identity authentication and key generation in resource-constrained devices. However, its response consistency is highly sensitive to environmental perturbations. The same challenge may therefore produce inconsistent responses under different conditions, which reduces the reliability of APUF-based security systems. Existing reliability improvement schemes mainly rely on hardware modification or challenge screening. These schemes often require high resource overhead and have low efficiency. To address these limitations, a Delay-constrained Challenge Generation Strategy (DCGS) is proposed to improve APUF reliability without additional hardware overhead or inefficient candidate screening. Methods DCGS models APUF path-delay characteristics and constructs challenges with constrained delay differences to ensure response stability. First, a Logistic Regression (LR) model is established to characterize the relationship between challenge bits and path delays. A delay-weight vector is then derived from the trained LR model to quantify the contribution of each challenge bit to the overall path delay. Second, a two-stage challenge generation mechanism is designed for delay-constraint control. In the first stage, prefix-bit initialization generates different prefix sequences to establish a delay baseline for subsequent bitwise extension. In the second stage, bitwise extension dynamically determines each remaining challenge bit according to the delay-weight vector. During this process, the cumulative delay difference of each challenge is monitored in real time and maintained within a preset delay-difference threshold range. Unlike conventional screening methods that post-process candidate challenges, DCGS directly generates stable challenges by design. This design removes the need for candidate challenge pools and improves generation efficiency. Results and Discussions DCGS is evaluated under different noise intensities. At a noise intensity of 0.3, which represents the maximum practical noise level, the reliability of DCGS-generated challenges remains 100% (Fig. 2). For generation efficiency, DCGS requires only 0.017 s to generate 10,000 challenges (Table 4). The response uniformity reaches 50.02% (Table 4), and the uniqueness reaches 50.46% (Table 4). Both metrics are close to the ideal theoretical value of 50%. The security analysis shows that the average bit entropy of DCGS-generated challenges is 0.980 7 (Fig. 3). The conditional entropy is 0.987 8, only 0.002 3 lower than that of random challenges (0.990 1). Conclusions This paper proposes DCGS for APUF to address inconsistent responses, low generation efficiency, and high hardware resource consumption in traditional schemes under high-noise conditions. By modeling path-delay characteristics with LR and combining prefix-bit initialization with bitwise extension, the proposed strategy ensures that the generated challenges satisfy the preset delay-difference threshold range. DCGS achieves high reliability, high efficiency, and good response uniformity without increasing hardware overhead. Experimental results show that DCGS improves APUF reliability in complex environments and supports secure applications in resource-constrained devices.
- Arbiter physical unclonable function,
- Reliability improvement,
- Machine learning,
- Generation strategy

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References(22)

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