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A Self-Oscillated Organic Synapse for In-Memory Two-Factor Authentication.

Shuzhi Liu1,2, Xiaolong Zhong1, Yuxuan Li2

  • 1Department of Micro/Nano Electronics, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|March 23, 2024
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Summary
This summary is machine-generated.

This study introduces in-memory two-factor authentication (IM-2FA) for AI target recognition. It effectively prevents unauthorized access by integrating asymmetric encryption with convolutional neural networks (ConvNets).

Keywords:
in‐memory asymmetric encryptionin‐memory two‐factor authenticationorganic synapseself‐oscillated characteristic

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Area of Science:

  • Neuromorphic Computing
  • Artificial Intelligence (AI)
  • Cybersecurity

Background:

  • AI 2.0 era necessitates robust bio-inspired target recognition.
  • Target recognition systems face risks of hijacking and unauthorized access.
  • Pre-authentication encryption is crucial before neuromorphic computing.

Purpose of the Study:

  • To develop an in-memory asymmetric encryption method for pre-authentication in target recognition.
  • To implement an in-memory two-factor authentication (IM-2FA) strategy.
  • To enhance the security of convolutional neural networks (ConvNets) used for target recognition.

Main Methods:

  • Utilized time-varied synaptic rules for in-memory asymmetric encryption.
  • Employed unipolar self-oscillated synaptic behavior for encryption, reducing peripheral circuit complexity.
  • Integrated encryption with a subsequent convolutional neural network (ConvNet) for target recognition.

Main Results:

  • Without proper encryption, ConvNet accuracy for target recognition dropped below 0.86%.
  • Successful encryption with correct weights at the appropriate time enabled a recognition rate of 99.82%.
  • Demonstrated effective blocking of involuntary access and validation of the IM-2FA strategy.

Conclusions:

  • The proposed IM-2FA strategy significantly enhances the security of AI-based target recognition.
  • In-memory asymmetric encryption effectively prevents unauthorized access prior to ConvNet processing.
  • The system offers a secure and efficient solution for pre-authentication in neuromorphic computing applications.