A Biometric-Based IoT Device Identity Authentication Scheme

Xue, Qing; Ju, Xingzhong; Zhu, Haozhi; Zhu, Haojin; Li, Fengying; Zheng, Xiangwei

doi:10.1007/978-3-030-22971-9_12

Cited by 3 publications

(1 citation statement)

References 4 publications

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“…Biometrics is one of the important factors that are presented for authentication. The fingerprint is the most used biometric for identifying individuals [ 12 ]. The fingerprint is encrypted and stored; further, during authentication, it is retrieved back for verification.…”

Section: Introductionmentioning

confidence: 99%

Two-Layered Multi-Factor Authentication Using Decentralized Blockchain in an IoT Environment

Bamashmos,

Chilamkurti,

Shahraki

2024

Sensors

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Internet of Things (IoT) technology is evolving over the peak of smart infrastructure with the participation of IoT devices in a wide range of applications. Traditional IoT authentication methods are vulnerable to threats due to wireless data transmission. However, IoT devices are resource- and energy-constrained, so building lightweight security that provides stronger authentication is essential. This paper proposes a novel, two-layered multi-factor authentication (2L-MFA) framework using blockchain to enhance IoT devices and user security. The first level of authentication is for IoT devices, one that considers secret keys, geographical location, and physically unclonable function (PUF). Proof-of-authentication (PoAh) and elliptic curve Diffie–Hellman are followed for lightweight and low latency support. Second-level authentication for IoT users, which are sub-categorized into four levels, each defined by specific factors such as identity, password, and biometrics. The first level involves a matrix-based password; the second level utilizes the elliptic curve digital signature algorithm (ECDSA); and levels 3 and 4 are secured with iris and finger vein, providing comprehensive and robust authentication. We deployed fuzzy logic to validate the authentication and make the system more robust. The 2L-MFA model significantly improves performance, reducing registration, login, and authentication times by up to 25%, 50%, and 25%, respectively, facilitating quicker cloud access post-authentication and enhancing overall efficiency.

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Section: Introductionmentioning

confidence: 99%

Two-Layered Multi-Factor Authentication Using Decentralized Blockchain in an IoT Environment

Bamashmos,

Chilamkurti,

Shahraki

2024

Sensors

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Biometric-Based Authentication in Internet of Things (IoT): A Review

Singh¹,

Kant²

2022

Advances in Information Communication Technology and Computing

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DeepKnuckle: Deep Learning for Finger Knuckle Print Recognition

et al. 2022

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Biometric technology has received a lot of attention in recent years. One of the most prevalent biometric traits is the finger-knuckle print (FKP). Because the dorsal region of the finger is not exposed to surfaces, FKP would be a dependable and trustworthy biometric. We provide an FKP framework that uses the VGG-19 deep learning model to extract deep features from FKP images in this paper. The deep features are collected from the VGG-19 model’s fully connected layer 6 (F6) and fully connected layer 7 (F7). After applying multiple preprocessing steps, such as combining features from different layers and performing dimensionality reduction using principal component analysis (PCA), the extracted deep features are put to the test. The proposed system’s performance is assessed using experiments on the Delhi Finger Knuckle Dataset employing a variety of common classifiers. The best identification result was obtained when the Artificial neural network (ANN) classifier was applied to the principal components of the averaged feature vector of F6 and F7 deep features, with 95% of the data variance preserved. The findings also demonstrate the feasibility of employing these deep features in an FKP recognition system.

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A Biometric-Based IoT Device Identity Authentication Scheme

Cited by 3 publications

References 4 publications

Two-Layered Multi-Factor Authentication Using Decentralized Blockchain in an IoT Environment

Two-Layered Multi-Factor Authentication Using Decentralized Blockchain in an IoT Environment

Biometric-Based Authentication in Internet of Things (IoT): A Review

DeepKnuckle: Deep Learning for Finger Knuckle Print Recognition

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