Deep PUF: A Highly Reliable DRAM PUF-Based Authentication for IoT Networks Using Deep Convolutional Neural Networks.

Fatemeh Najafi,Diego Martín,Mohammad Reza Mosavi,Masoud Kaveh

doi:10.3390/s21062009

Abstract

Traditional authentication techniques, such as cryptographic solutions, are vulnerable to various attacks occurring on session keys and data. Physical unclonable functions (PUFs) such as dynamic random access memory (DRAM)-based PUFs are introduced as promising security blocks to enable cryptography and authentication services. However, PUFs are often sensitive to internal and external noises, which cause reliability issues. The requirement of additional robustness and reliability leads to the involvement of error-reduction methods such as error correction codes (ECCs) and pre-selection schemes that cause considerable extra overheads. In this paper, we propose deep PUF: a deep convolutional neural network (CNN)-based scheme using the latency-based DRAM PUFs without the need for any additional error correction technique. The proposed framework provides a higher number of challenge-response pairs (CRPs) by eliminating the pre-selection and filtering mechanisms. The entire complexity of device identification is moved to the server side that enables the authentication of resource-constrained nodes. The experimental results from a 1Gb DDR3 show that the responses under varying conditions can be classified with at least a 94.9% accuracy rate by using CNN. After applying the proposed authentication steps to the classification results, we show that the probability of identification error can be drastically reduced, which leads to a highly reliable authentication.

Highlights

A large number of modern cryptographic protocols are based on physical unclonable functions (PUF) implementations, which are used for key agreement and device authentication [1,2,3,4,5]
We propose deep PUF that utilizes all latency failures in different Dynamic random access memory (DRAM) blocks to expand the challenge-response pairs (CRPs) space and organize a robust authentication without employing any pre-selection algorithms or post-correction mechanisms
We present a new DRAM PUF-based authentication method that exploits a deep convolutional neural network (CNN) to configure a strong PUF with an expanded CRP space and light-weight implementation

Summary

Introduction

Published: 12 March 2021A large number of modern cryptographic protocols are based on physical unclonable functions (PUF) implementations, which are used for key agreement and device authentication [1,2,3,4,5]. Dynamic random access memory (DRAM)-based PUFs provide large address space and utilize several controllable properties to generate unique identifiers for identification and authentication purposes [8,9,10,11]. Most of the existing PUFs use some post-processing techniques, which require helper data algorithms and complex error correction codes (ECCs) to extract reliable responses and conduct a proper authentication procedure [8,14]. These methods cause significant hardware/computational overheads and additional Non-volatile memory (NVM) to store helper data in addition to their security defects [15,16,17]. All bitlines are precharged, and other open wordlines are deactivated after using the

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