A ReRAM Physically Unclonable Function (ReRAM PUF)-Based Approach to Enhance Authentication Security in Software Defined Wireless Networks

Fatemeh Afghah, Bertrand Cambou, Masih Abedini, Sherali Zeadally

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


The exponentially increasing number of ubiquitous wireless devices connected to the Internet in Internet of Things (IoT) networks highlights the need for a new paradigm of data flow management in such large-scale networks under software defined wireless networking (SDWN). The limited power and computation capability available at IoT devices as well as the centralized management and decision making approach in SDWN introduce a whole new set of security threats to the networks. In particular, the authentication mechanism between the controllers and the forwarding devices in SDWNs is a key challenge from both secrecy and integrity aspects. Conventional authentication protocols based on public key infrastructure (PKI) are no longer sufficient for these networks considering the large-scale and heterogeneity nature of the networks as well as their deployment cost, and security vulnerabilities due to key distribution and storage. We propose a novel security protocol based on physical unclonable functions (PUFs) known as hardware security primitives to enhance the authentication security in SDWNs. In this approach, digital PUFs are developed using the inherent randomness of the nanomaterials of Resistive Random Access Memory (ReRAM) that are embedded in most IoT devices to enable a secure authentication and access control in these networks. These PUFs are developed based on a novel approach of multi states, in which the natural drifts due to the physical variations in the environment are predicted to reduce the potential errors in challenge-response pairs of PUFs being tested in different situations. We also proposed a PUF-based PKI protocol to secure the controller in SDWNs. The performance of the developed ReRAM-based PUFs are evaluated in the experimental results. Moreover, the effectiveness of the proposed multi-state machine learning technique to predict the drifts of the PUFs’ responses in different temperature and biased conditions is presented.

Original languageEnglish (US)
Pages (from-to)117-129
Number of pages13
JournalInternational Journal of Wireless Information Networks
Issue number2
StatePublished - Jun 1 2018


  • Authentication
  • Hardware security
  • Internet of Things
  • Physically unclonable functions
  • Resistive RAM
  • Software defined wireless networking

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Networks and Communications
  • Electrical and Electronic Engineering


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