Related Experiment Video
Updated: Nov 10, 2025
13:19
Deep Neural Networks for Image-Based Dietary Assessment
Published on: March 13, 2021
9.6K
Deep PUF: A Highly Reliable DRAM PUF-Based Authentication for IoT Networks Using Deep Convolutional Neural Networks.
Fatemeh Najafi1, Masoud Kaveh2, Diego Martín1
1ETSI de Telecomunicación, Universidad Politécnica de Madrid, Av. Complutense 30, 28040 Madrid, Spain.
Sensors (Basel, Switzerland)
|April 3, 2021
Summary
This study introduces Deep PUF, a novel authentication method using deep convolutional neural networks (CNNs) with dynamic random access memory (DRAM) Physical Unclonable Functions (PUFs). It achieves reliable device authentication without error correction, enhancing security for resource-constrained devices.
Area of Science:
- Computer Science
- Cybersecurity
- Hardware Security
Background:
- Traditional cryptographic authentication methods are vulnerable to attacks on session keys and data.
- Physical Unclonable Functions (PUFs), specifically DRAM-based PUFs, offer a promising alternative for secure authentication.
- Existing PUF implementations often suffer from reliability issues due to noise, necessitating complex error-reduction techniques and increasing overhead.
Purpose of the Study:
- To propose a novel authentication scheme, Deep PUF, leveraging deep convolutional neural networks (CNNs) and latency-based DRAM PUFs.
- To eliminate the need for additional error correction codes (ECCs) or pre-selection schemes in PUF-based authentication.
- To enhance the number of challenge-response pairs (CRPs) and enable authentication for resource-constrained devices by offloading complexity to the server.
Main Methods:
- Implementation of a deep convolutional neural network (CNN) for classifying responses from latency-based DRAM PUFs.
- Utilizing a 1Gb DDR3 memory module for experimental validation.
- Developing an authentication framework that integrates CNN classification results to minimize identification errors.
Main Results:
- The proposed CNN model achieved a classification accuracy of at least 94.9% for DRAM PUF responses under varying conditions.
- The Deep PUF scheme successfully eliminated the need for traditional error-reduction methods like ECCs and pre-selection.
- Experimental results demonstrated a drastic reduction in the probability of identification error, leading to highly reliable authentication.
Conclusions:
- Deep PUF offers a robust and reliable authentication solution for devices, particularly those with limited resources.
- The integration of CNNs with DRAM PUFs significantly improves authentication security and efficiency.
- This approach overcomes the inherent reliability challenges of PUFs without introducing substantial overhead.
