A fingerprint recognition framework using Artificial Neural Network

Oulhiq, Ridouane; Ibntahir, Saad; Sebgui, Marouane; Guennoun, Zouhair

doi:10.1109/sita.2015.7358382

Cited by 11 publications

(5 citation statements)

References 5 publications

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“…In [185] a framework has been proposed for using ANN to identify fingerprints digitally. The digital values of various features in the fingerprint like minutiae, ridge ending, and bifurcation is applied as the input to the neural network for training purpose using back propagation algorithm of ANN.…”

Section: Digital Fingerprintingmentioning

confidence: 99%

A Survey on IoT Security: Application Areas, Security Threats, and Solution Architectures

et al. 2019

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The Internet of Things (IoT) is the next era of communication. Using the IoT, physical objects can be empowered to create, receive, and exchange data in a seamless manner. Various IoT applications focus on automating different tasks and are trying to empower the inanimate physical objects to act without any human intervention. The existing and upcoming IoT applications are highly promising to increase the level of comfort, efficiency, and automation for the users. To be able to implement such a world in an evergrowing fashion requires high security, privacy, authentication, and recovery from attacks. In this regard, it is imperative to make the required changes in the architecture of the IoT applications for achieving end-to-end secure IoT environments. In this paper, a detailed review of the security-related challenges and sources of threat in the IoT applications is presented. After discussing the security issues, various emerging and existing technologies focused on achieving a high degree of trust in the IoT applications are discussed. Four different technologies, blockchain, fog computing, edge computing, and machine learning, to increase the level of security in IoT are discussed. INDEX TERMS Internet of Things (IoT), IoT security, blockchain, fog computing, edge computing, machine learning, IoT applications, distributed systems.

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Section: Digital Fingerprintingmentioning

confidence: 99%

A Survey on IoT Security: Application Areas, Security Threats, and Solution Architectures

et al. 2019

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“…The solutions of machine learning are far away from conventional methods, e.g., pulse swarm optimization and backpropagation [205] are new trends with promising solutions in the field. The use of neural networks [206] and learning-based algorithms may enhance the concept eventually [207]- [218].…”

Section: Machine Learningmentioning

confidence: 99%

Security in Internet of Things: A Review

2022

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Internet of Things (IoT) is the paramount virtual network that enables remote users to access connected multimedia devices. It has dragged the attention of the community because it encompasses realworld scenarios with implicit environs. Despite several beneficial aspects, IoT is surrounded by provocations for successful implementation, as data travels in different layers. One of the critical challenges is the security of the data in these layers. Researchers conducted numerous studies focused on the level of security at a single technique, creating loopholes to address the entire scenario of securing an IoT network. This study aims to comprehensively review current security issues, wireless communication techniques, and technologies for securing IoT. This work's utmost significance is addressing all the security perspectives at a glance. For this purpose, research contributions from the previous years are investigated for better understanding. Some countermeasures and snags from security perspectives have also been analyzed in detail concerning the current industry trends. Blockchain, machine learning, fog, and edge computing are possible solutions to secure IoT. After studying these techniques and their immunity to attacks, machine learning can become a hope if incorporated with end-to-end security. This comprehensive review will provide adequate understanding and knowledge in defining security lines of action for the successful implementation of IoT.

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“…These days, fraud identification in smart environments is considered one of the most criminal security issues. To prohibit these issues, many methods are utilized like the techniques of biometrics identification, because of the necessity of extremely reliable security methods in confidential systems [ 5 ]. Biometric identification systems are highly used in several public security systems [ 6 , 7 ] such as surveillance, recognition, law enforcement to attain superior recognition performance [ 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

Enhanced multimodal biometric recognition systems based on deep learning and traditional methods in smart environments

A. El_Rahman,

Alluhaidan

2024

PLoS ONE

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In the field of data security, biometric security is a significant emerging concern. The multimodal biometrics system with enhanced accuracy and detection rate for smart environments is still a significant challenge. The fusion of an electrocardiogram (ECG) signal with a fingerprint is an effective multimodal recognition system. In this work, unimodal and multimodal biometric systems using Convolutional Neural Network (CNN) are conducted and compared with traditional methods using different levels of fusion of fingerprint and ECG signal. This study is concerned with the evaluation of the effectiveness of proposed parallel and sequential multimodal biometric systems with various feature extraction and classification methods. Additionally, the performance of unimodal biometrics of ECG and fingerprint utilizing deep learning and traditional classification technique is examined. The suggested biometric systems were evaluated utilizing ECG (MIT-BIH) and fingerprint (FVC2004) databases. Additional tests are conducted to examine the suggested models with:1) virtual dataset without augmentation (ODB) and 2) virtual dataset with augmentation (VDB). The findings show that the optimum performance of the parallel multimodal achieved 0.96 Area Under the ROC Curve (AUC) and sequential multimodal achieved 0.99 AUC, in comparison to unimodal biometrics which achieved 0.87 and 0.99 AUCs, for the fingerprint and ECG biometrics, respectively. The overall performance of the proposed multimodal biometrics outperformed unimodal biometrics using CNN. Moreover, the performance of the suggested CNN model for ECG signal and sequential multimodal system based on neural network outperformed other systems. Lastly, the performance of the proposed systems is compared with previously existing works.

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A fingerprint recognition framework using Artificial Neural Network

Cited by 11 publications

References 5 publications

A Survey on IoT Security: Application Areas, Security Threats, and Solution Architectures

A Survey on IoT Security: Application Areas, Security Threats, and Solution Architectures

Security in Internet of Things: A Review

Enhanced multimodal biometric recognition systems based on deep learning and traditional methods in smart environments

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