Implementing hardware primitives based on memristive spatiotemporal variability into cryptography applications
Bo Liu,
Yudi Zhao,
YinFeng Chang,
Han Hsiang Tai,
Hanyuan Liang,
Tsung-Cheng Chen,
Shiwei Feng,
Tuo-Hung Hou,
Chao-Sung Lai
Affiliations
Bo Liu
Faculty of Information Technology, College of Microelectronics, Beijing University of Technology, Beijing 100124, Beijing, China; Corresponding author.
Yudi Zhao
School of Information and Communication Engineering, Beijing Information Science & Technology University, Beijing 100101, Beijing, China
YinFeng Chang
Artificial Intelligence and Green Technology Research Center, Chang Gung University, Guishan Dist., Taoyuan 33302, Taiwan, China
Han Hsiang Tai
Department of Electronic Engineering, Chang Gung University, Guishan Dist., Taoyuan 33302, Taiwan, China
Hanyuan Liang
School of Electrical Engineering and Computer Science, The Pennsylvania State University, University Park, PA 16802, USA
Tsung-Cheng Chen
Department of Electronic Engineering, Chang Gung University, Guishan Dist., Taoyuan 33302, Taiwan, China
Shiwei Feng
Faculty of Information Technology, College of Microelectronics, Beijing University of Technology, Beijing 100124, Beijing, China
Tuo-Hung Hou
Institute of Electronics, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan, China
Chao-Sung Lai
Artificial Intelligence and Green Technology Research Center, Chang Gung University, Guishan Dist., Taoyuan 33302, Taiwan, China; Department of Electronic Engineering, Chang Gung University, Guishan Dist., Taoyuan 33302, Taiwan, China; Department of Nephrology, Chang Gung Memorial Hospital, Guishan Dist., Linkou 33305, Taiwan, China; Department of Materials Engineering, Ming Chi University of Technology, Taishan Dist., New Taipei City 24301, Taiwan, China; Corresponding author at: Artificial Intelligence and Green Technology Research Center, Chang Gung University, Guishan Dist., Taoyuan 33302, Taiwan.
Implementing hardware primitives into cryptosystem has become a new trend in electronic community. Memristor, with intrinsic stochastic characteristics including the switching voltages, times and energies, as well as the fluctuations of the resistance state over time, could be a naturally good entropy source for cryptographic key generation. In this study, based on kinetic Monte Carlo Simulation, multiple Artificial Intelligence techniques, as well as kernel density map and time constant analysis, memristive spatiotemporal variability within graphene based conductive bridging RAM (CBRAM) have been synergistically analyzed to verify the inherent randomness of the memristive stochasticity. Moreover, the random number based on hardware primitives passed the Hamming Distance calculation with high randomness and uniqueness, and has been integrated into a Rivest-Shamir-Adleman (RSA) cryptosystem. The security of the holistic cryptosystem relies both the modular arithmetic algorithm and the intrinsic randomness of the hardware primitive (to be more reliable, the random number could be as large as possible, better larger than 2048 bits as NIST suggested). The spatiotemporal-variability-based random number is highly random, physically unpredictable and machine-learning-attack resilient, improving the robustness of the entire cryptosystem.
