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

机译:基于ReRAM物理不可克隆功能(ReRAM PUF)的方法,可增强软件定义的无线网络中的身份验证安全性

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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.
机译:物联网(IoT)网络中连接到Internet的无处不在的无线设备的数量呈指数增长,这凸显了在这种软件定义的无线网络(SDWN)下的大规模网络中对数据流管理的新范例的需求。物联网设备可用的功能和计算能力有限,以及SDWN中的集中式管理和决策方法,给网络带来了全新的安全威胁。特别地,从保密和完整性方面来看,SDWN中的控制器和转发设备之间的身份验证机制都是一个关键挑战。考虑到网络的大规模和异构性,部署成本以及密钥分发和存储带来的安全漏洞,基于公钥基础结构(PKI)的常规身份验证协议已不足以满足这些网络的需要。我们提出了一种基于物理不可克隆功能(PUF)的新颖安全协议,称为物理安全性原语,以增强SDWN中的身份验证安全性。在这种方法中,数字PUF是使用嵌入在大多数IoT设备中的电阻随机存取存储器(ReRAM)纳米材料的固有随机性开发的,以实现这些网络中的安全身份验证和访问控制。这些PUF是基于一种新颖的多状态方法开发的,其中预测了由于环境物理变化而引起的自然漂移,以减少在不同情况下测试的PUF的挑战响应对中的潜在误差。我们还提出了基于PUF的PKI协议来保护SDWN中的控制器。在实验结果中评估了基于ReRAM的已开发PUF的性能。此外,还提出了所提出的多状态机器学习技术在不同温度和偏置条件下预测PUF响应漂移的有效性。

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