A Cross-Layer Biometric Recognition System for Mobile IoT Devices

Taheri, Shayan; Yuan, J.S.

doi:10.3390/electronics7020026

Cited by 12 publications

(7 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There has been a lot of study done on FKP recognition for both identification and authentication security, whether for IoT or more general security [116][117][118][119][120][121]. These are only a few instances.…”

Section: Related Workmentioning

confidence: 99%

DeepKnuckle: Deep Learning for Finger Knuckle Print Recognition

et al. 2022

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%

DeepKnuckle: Deep Learning for Finger Knuckle Print Recognition

et al. 2022

View full text Add to dashboard Cite

show abstract

“…In this paper, a fingerprint-based authentication system is implemented with Raspberry Pi together with several types of sensors. Taheri and Yuan [28] developed a cross-layer biometric recognition system that includes both software and hardware layers in a chaining structure. In this work, biometrics (e.g., fingerprint) is adopted.…”

Section: Single-modal Biometric Authentication Systemsmentioning

confidence: 99%

Biometrics for Internet-of-Things Security: A Review

Yang

Wang

Sahri

et al. 2021

Sensors

View full text Add to dashboard Cite

The large number of Internet-of-Things (IoT) devices that need interaction between smart devices and consumers makes security critical to an IoT environment. Biometrics offers an interesting window of opportunity to improve the usability and security of IoT and can play a significant role in securing a wide range of emerging IoT devices to address security challenges. The purpose of this review is to provide a comprehensive survey on the current biometrics research in IoT security, especially focusing on two important aspects, authentication and encryption. Regarding authentication, contemporary biometric-based authentication systems for IoT are discussed and classified based on different biometric traits and the number of biometric traits employed in the system. As for encryption, biometric-cryptographic systems, which integrate biometrics with cryptography and take advantage of both to provide enhanced security for IoT, are thoroughly reviewed and discussed. Moreover, challenges arising from applying biometrics to IoT and potential solutions are identified and analyzed. With an insight into the state-of-the-art research in biometrics for IoT security, this review paper helps advance the study in the field and assists researchers in gaining a good understanding of forward-looking issues and future research directions.

show abstract

“…In attaining this IoT vision, Low-Power (LP) and Loss Networks (LLNs) are diversified, and the interconnection of restricted physical devices by the use of LP and LLNs involves the modification of protocols and existing structures currently in common use [23]. Latterly, hardware trojan attacks have developed as a threat to all hardware and integrated circuits (ICs) [24]. The main challenge of handling network connectivity in a tightly-equipped setting, including a smart factory, is to identify and manipulate different attack vectors.…”

Section: Multi-layer Security Modelmentioning

confidence: 99%

Hardware-Intrinsic Multi-Layer Security: A New Frontier for 5G Enabled IIoT

Al-Aqrabi

Johnson

Hill

et al. 2020

Sensors

View full text Add to dashboard Cite

The introduction of 5G communication capabilities presents additional challenges for the development of products and services that can fully exploit the opportunities offered by high bandwidth, low latency networking. This is particularly relevant to an emerging interest in the Industrial Internet of Things (IIoT), which is a foundation stone of recent technological revolutions such as Digital Manufacturing. A crucial aspect of this is to securely authenticate complex transactions between IIoT devices, whilst marshalling adversarial requests for system authorisation, without the need for a centralised authentication mechanism which cannot scale to the size needed. In this article we combine Physically Unclonable Function (PUF) hardware (using Field Programmable Gate Arrays—FPGAs), together with a multi-layer approach to cloud computing from the National Institute of Standards and Technology (NIST). Through this, we demonstrate an approach to facilitate the development of improved multi-layer authentication mechanisms. We extend prior work to utilise hardware security primitives for adversarial trojan detection, which is inspired by a biological approach to parameter analysis. This approach is an effective demonstration of attack prevention, both from internal and external adversaries. The security is further hardened through observation of the device parameters of connected IIoT equipment. We demonstrate that the proposed architecture can service a significantly high load of device authentication requests using a multi-layer architecture in an arbitrarily acceptable time of less than 1 second.

show abstract

A Cross-Layer Biometric Recognition System for Mobile IoT Devices

Cited by 12 publications

References 19 publications

DeepKnuckle: Deep Learning for Finger Knuckle Print Recognition

DeepKnuckle: Deep Learning for Finger Knuckle Print Recognition

Biometrics for Internet-of-Things Security: A Review

Hardware-Intrinsic Multi-Layer Security: A New Frontier for 5G Enabled IIoT

Contact Info

Product

Resources

About