PMID- 34665596
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20221109
IS  - 1936-086X (Electronic)
IS  - 1936-0851 (Linking)
VI  - 15
IP  - 11
DP  - 2021 Nov 23
TI  - A Machine Learning Attack Resilient True Random Number Generator Based on 
      Stochastic Programming of Atomically Thin Transistors.
PG  - 17804-17812
LID - 10.1021/acsnano.1c05984 [doi]
AB  - A true random number generator (TRNG) is a critical hardware component that has 
      become increasingly important in the era of Internet of Things (IoT) and mobile 
      computing for ensuring secure communication and authentication schemes. While 
      recent years have seen an upsurge in TRNGs based on nanoscale materials and 
      devices, their resilience against machine learning (ML) attacks remains 
      unexamined. In this article, we demonstrate a ML attack resilient, low-power, and 
      low-cost TRNG by exploiting stochastic programmability of floating gate (FG) 
      field effect transistors (FETs) with atomically thin channel materials. The 
      origin of stochasticity is attributed to the probabilistic nature of charge 
      trapping and detrapping phenomena in the FG. Our TRNG also satisfies other 
      requirements, which include high entropy, uniformity, uniqueness, and 
      unclonability. Furthermore, the generated bit-streams pass NIST randomness tests 
      without any postprocessing. Our findings are important in the context of hardware 
      security for resource constrained IoT edge devices, which are becoming 
      increasingly vulnerable to ML attacks.
FAU - Wali, Akshay
AU  - Wali A
AD  - Electrical Engineering and Computer Science, Penn State University, University 
      Park, Pennsylvania 16802, United States.
FAU - Ravichandran, Harikrishnan
AU  - Ravichandran H
AD  - Engineering Science and Mechanics, Penn State University, University Park, 
      Pennsylvania 16802, United States.
FAU - Das, Saptarshi
AU  - Das S
AUID- ORCID: 0000-0002-0188-945X
AD  - Electrical Engineering and Computer Science, Penn State University, University 
      Park, Pennsylvania 16802, United States.
AD  - Engineering Science and Mechanics, Penn State University, University Park, 
      Pennsylvania 16802, United States.
AD  - Materials Science and Engineering, Penn State University, University Park, 
      Pennsylvania 16802, United States.
AD  - Materials Research Institute, Pennsylvania State University, University Park, 
      Pennsylvania 16802, United States.
LA  - eng
PT  - Journal Article
DEP - 20211019
PL  - United States
TA  - ACS Nano
JT  - ACS nano
JID - 101313589
SB  - IM
OTO - NOTNLM
OT  - Internet of things
OT  - charge trapping/detrapping
OT  - field effect transistors
OT  - floating gate
OT  - hardware security
OT  - machine learning
OT  - random numbers
EDAT- 2021/10/20 06:00
MHDA- 2021/10/20 06:01
CRDT- 2021/10/19 17:09
PHST- 2021/10/20 06:00 [pubmed]
PHST- 2021/10/20 06:01 [medline]
PHST- 2021/10/19 17:09 [entrez]
AID - 10.1021/acsnano.1c05984 [doi]
PST - ppublish
SO  - ACS Nano. 2021 Nov 23;15(11):17804-17812. doi: 10.1021/acsnano.1c05984. Epub 2021 
      Oct 19.