Abdulaziz Al-Meer

Mr. Abdulaziz Al-Meer

orcid

Research Focus & Metrics

Academic Career Length

4Years (2023-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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17 July 2023 in ACM Computing Surveys
Verified
Journal Article
Open access
AAAbdulaziz Al-Meer
Abdulaziz Al-Meer
SASaif Al-Kuwari
Saif Al-Kuwari
Physical Unclonable Function (PUF) has recently attracted interest from both industry and academia as a potential alternative approach to secure Internet of Things (IoT) devices from the more traditional computational-based approach using conventional cryptography. PUF is a promising solution for lightweight security, where the manufacturing fluctuation process of IC is used to improve the security of IoT as it provides low complexity design and preserves secrecy. PUF provides a low-cost low-power solution and can be implemented in both Field Programmable Gate Arrays (FPGA) and Application-Specific Integrated Circuits (ASICs). In this survey, we provide a comprehensive review of the state-of-the-art of PUF, its architectures, protocols and security for IoT.

26 November 2025 in Electronics
Verified
Journal Article
Open access
PDF file
AAAbdulaziz Al-Meer
Abdulaziz Al-Meer
SASaif Al-Kuwari
Saif Al-Kuwari
Physical Unclonable Functions (PUFs) are hardware-based security primitives that can produce unique digital identifiers from electronic devices. They are particularly useful for Internet of Things (IoT) applications due to their low cost and ability to improve security on lightweight devices. In this paper, we propose a new lightweight delay-based Linear Feedback Shift Register (LFSR) PUF with configurable primitive feedback. Our configurable PUF offers various important benefits, such as a compact architecture, low hardware overhead, a large challenge-response space, conservative power requirements, and flexibility to operate in different modes. We implement our proposed PUF on an FPGA, and the experimental results demonstrate that our PUF exhibits nearly ideal performance metrics in terms of uniformity and uniqueness, with minimal hardware overhead and low power consumption. Moreover, our PUF also passes the National Institute of Standards and Technology (NIST) statistical test suite. We also show that our proposed PUF is resistant to Machine Learning (ML) attacks.