ANN Approach to Predict the Responses of 64 bits XOR APUF with High Accuracy
Author:
Affiliation:
1. Farmingdale State College,Electrical and Computer Engineering Technology,NY,USA
2. University of Akron,School of Polymer Science and Polymer Engineering,Akron,OH,USA
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10467192/10468068/10468207.pdf?arnumber=10468207
Reference20 articles.
1. XOR-Based Low-Cost Reconfigurable PUFs for IoT Security
2. Noise-Resilient Neural Network-Based Adversarial Attack Modeling for XOR Physical Unclonable Functions
3. A PUF taxonomy
4. Novel strong PUF based on nonlinearity of MOSFET subthreshold operation
5. PUFs: Myth, Fact or Busted? A Security Evaluation of Physically Unclonable Functions (PUFs) Cast in Silicon
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