TY - JOUR
T1 - A robust architecture of physical unclonable function based on Memristor crossbar array
AU - Khan, Muhammad Ibrar
AU - Ali, Shawkat
AU - Al-Tamimi, Aref
AU - Hassan, Arshad
AU - Ikram, Ataul Aziz
AU - Bermak, Amine
N1 - Publisher Copyright:
© 2021
PY - 2021/10
Y1 - 2021/10
N2 - During the past two decades, Physical Unclonable Functions (PUF) remained under discussion as strong hardware security primitives. As compared to conventional technology, memristor-based PUFs got tremendous attraction due to their simple and easy architecture, high endurance, low energy consumption, small size, and low cost. The inherent high variability of memristive devices makes it an ideal candidate for the generation of unique fingerprints for individual devices. By exploiting this variation, we propose a novel architecture dual memristive crossbar (DuMXbar) that integrates two memristive crossbar PUFs implemented with two different memristor devices. Each PUF generates highly uncorrelated response pairs. The simulation results of DuMXbar PUF prove that it is comparatively more robust against machine learning (ML) based classification algorithms like logistic regression and support vector machine. We have also proposed a secure authentication protocol for DuMXbar PUF to restrict the prediction accuracy of ML algorithms close down to 50% (ideal).
AB - During the past two decades, Physical Unclonable Functions (PUF) remained under discussion as strong hardware security primitives. As compared to conventional technology, memristor-based PUFs got tremendous attraction due to their simple and easy architecture, high endurance, low energy consumption, small size, and low cost. The inherent high variability of memristive devices makes it an ideal candidate for the generation of unique fingerprints for individual devices. By exploiting this variation, we propose a novel architecture dual memristive crossbar (DuMXbar) that integrates two memristive crossbar PUFs implemented with two different memristor devices. Each PUF generates highly uncorrelated response pairs. The simulation results of DuMXbar PUF prove that it is comparatively more robust against machine learning (ML) based classification algorithms like logistic regression and support vector machine. We have also proposed a secure authentication protocol for DuMXbar PUF to restrict the prediction accuracy of ML algorithms close down to 50% (ideal).
KW - Hardware security
KW - Machine learning aided IoT security
KW - Memristive PUF
KW - Memristor
KW - PUF based Authentication
KW - Physical unclonable function
UR - http://www.scopus.com/inward/record.url?scp=85114179324&partnerID=8YFLogxK
U2 - 10.1016/j.mejo.2021.105238
DO - 10.1016/j.mejo.2021.105238
M3 - Article
AN - SCOPUS:85114179324
SN - 0026-2692
VL - 116
JO - Microelectronics Journal
JF - Microelectronics Journal
M1 - 105238
ER -