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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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A Machine Learning Attack Resilient True Random Number Generator Based on Stochastic Programming of Atomically Thin

Akshay Wali1, Harikrishnan Ravichandran2, Saptarshi Das1,2,3,4

  • 1Electrical Engineering and Computer Science, Penn State University, University Park, Pennsylvania 16802, United States.

ACS Nano
|October 19, 2021
PubMed
Summary
This summary is machine-generated.

This study presents a new true random number generator (TRNG) for Internet of Things (IoT) devices that is resilient to machine learning (ML) attacks. The low-power, low-cost design utilizes floating gate transistors for enhanced hardware security.

Keywords:
Internet of thingscharge trapping/detrappingfield effect transistorsfloating gatehardware securitymachine learningrandom numbers

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Area of Science:

  • Hardware security
  • Nanoscale devices
  • Random number generation

Background:

  • True random number generators (TRNGs) are crucial for secure communication in IoT and mobile computing.
  • Existing nanoscale TRNGs lack examination regarding their resilience against machine learning (ML) attacks.
  • Resource-constrained IoT edge devices are increasingly vulnerable to sophisticated ML-based attacks.

Purpose of the Study:

  • To develop a machine learning (ML) attack resilient, low-power, and low-cost true random number generator (TRNG).
  • To exploit the stochastic programmability of floating gate (FG) field effect transistors (FETs) with atomically thin channel materials for TRNG applications.

Main Methods:

  • Exploited stochastic programmability of floating gate (FG) FETs with atomically thin channels.
  • Attributed stochasticity to probabilistic charge trapping/detrapping phenomena in the FG.
  • Evaluated TRNG performance against key randomness metrics (entropy, uniformity, uniqueness, unclonability).

Main Results:

  • Demonstrated a ML attack resilient, low-power, and low-cost TRNG.
  • Achieved high entropy, uniformity, uniqueness, and unclonability.
  • Generated bit-streams passed NIST randomness tests without postprocessing.

Conclusions:

  • The developed TRNG offers a viable hardware security solution for resource-constrained IoT edge devices.
  • The findings address the critical need for ML attack resilience in emerging electronic systems.
  • Exploiting nanoscale device physics provides a pathway for robust and secure random number generation.