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Deep hashing with multilayer CNN-based biometric authentication for identifying individuals in transportation security

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Abstract

One of the biggest challenges in transportation security systems is ensuring reliable and effective identification of people. However, improved security in transportation systems was not achieved using the traditional biometric authentication methods. The objective of this research is to enhance the safety of transportation security systems through the deployment of a deep learning-based multimodal biometric authentication network (MMBA-Net). The network gathers biometric data, employs multilayer convolutional neural networks (ML-CNN) architecture to extract both low-level and high-level features, and extracts features using Deep Hashing Component Analysis (DHCA). An ML-CNN classifier is used to train the binary codes created from the retrieved features after they have been compressed. Large-scale dataset experiments demonstrate that DHCA performs better for accurate biometric authentication than state-of-the-art techniques.

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Data availability

No datasets were generated or analyzed during the current study.

Code availability

Not applicable.

Abbreviations

MMBA-Net:

Multimodal biometric authentication network

ML-CNN:

Multilayer convolutional neural networks

DHCA:

Deep Hashing Component Analysis

OTPs:

One-time passwords

IoT:

Internet of Things

BMFAM:

Blockchain-Based Multi-Factor Authentication Mechanism

MLDL:

Machine learning and deep learning

CAV-CNN:

Cybersecurity of Autonomous Vehicles based CNN

MLSC-DCNN:

Mutant leader sine cosine algorithm-based deep CNN

DCNN's:

Deep convolutional neural networks

HMBS:

Health monitoring-based systems

ECG:

Electrocardiogram

LSTM:

Long-short term memory

GRU:

Gated Recurrent Unit

FKP:

Finger knuckle print

DCA:

Discriminant Correlation Analysis

PPG:

Photoplethysmogram

CCA:

Canonicall correlation analysis

LDA:

Linear discriminant analysis

MICBTDL:

Multi-instance cancellable iris system

NIST:

National Institute of Standards and Technology

NBIS:

NIST Biometric Image Software

MCC:

Matthews Correlation Coefficient

RRIS:

Remote Radiology Information System

CASIA:

Chinese Academy of Sciences Institute of Automation

BioID:

Biometric Identification

CNN-SRU:

Convolutional Neural Network—Simple Recurrent Unit

DSS:

Decision Support System

ES:

Expert System

References

  • Abhijith, Kumar AS, Athira JV et al (2023) Finger vein biometric recognition system using deep learning. Int Res J Innov Eng Technol 7(3):166–169. https://doi.org/10.47001/IRJIET/2023.703025 

  • Abidi MH, Alkhalefah H, Mohammed MK (2022) Mutated leader sine-cosine algorithm for secure smart IoT-blockchain of industry 4.0. Comput Mater Continua 73(3):5367–5383. https://doi.org/10.32604/cmc.2022.030018

  • Adewole KS, Mojeed HA Ogunmodede JA et al (2022) Expert system and decision support system for electrocardiogram interpretation and diagnosis: review, challenges and research directions. Appl Sci 12(23):12342. https://doi.org/10.3390/app122312342

  • Aggarwal K, Khoa BT, Divya Sagar KV et al (2023) Marketing information system based on unsupervised visual data to manage transportation industry using signal processing. Expert Syst e13384. https://doi.org/10.1111/exsy.13384

  • Ahmad MO, Tripathi G, Siddiqui F et al (2023) BAuth-ZKP—A blockchain-based multi-factor authentication mechanism for securing smart cities. Sensors 23(5):2757. https://doi.org/10.3390/s23052757

  • Aldweesh A, Kodati S, AlauthmanM et al (2023) Mlora-CBF: efficient cluster-based routing protocol against resource allocation using modified location routing algorithm with cluster-based flooding. Wireless Netw 1–23. https://doi.org/10.1007/s11276-023-03506-2

  • Ali MH, Jaber MM, Alfred Daniel J et al (2023) Autonomous vehicles decision-making enhancement using self-determination theory and mixed-precision neural networks. Multimed Tools Appl 1–24. https://doi.org/10.1007/s11042-023-14375-4

  • Anand A, Kumar Singh A (2022) A comprehensive study of deep learning-based covert communication. ACM Trans Multimed Comput Commun Appl (TOMM) 18(2s):1–19. https://doi.org/10.1145/3508365

  • Aryanmehr S, Boroujeni FZ (2023) Efficient deep CNN-based gender classification using Iris wavelet scattering. Multimed Tools Appl 82(12):19041–19065. https://doi.org/10.1007/s11042-022-14062-w

  • Ashok K, Gopikrishnan S (2023) Statistical Analysis of Remote Health Monitoring Based IoT Security Models & Deployments From a Pragmatic Perspective. IEEE Access 11:2621–2651

    Article  Google Scholar 

  • BalaAnand M, Sivaparthipan CB (2013) “Security Privilege By Generating Random Selective Index In Session Based Password Authentication”. J Sci Innov Eng Technol

  • Bandi SR, Anbarasan M, Sheela D (2022) Fusion of SAR and optical images using pixel-based CNN. Neural Netw World 32(4):197–203. https://doi.org/10.14311/NNW.2022.32.012

  • Batool A, Nisar MW, Khan MA et al (2023) Traffic sign recognition using proposed lightweight twig-net with linear discriminant classifier for biometric application. Image Vision Comput 135:104711. https://doi.org/10.1016/j.imavis.2023.104711

  • Bharati S, Podder P (2022) Machine and deep learning for iot security and privacy: applications, challenges, and future directions. Secur Commun Netw 2022:1–41

    Article  Google Scholar 

  • Boucetta A, Boussaad L (2022) Biometric authentication using finger-vein patterns with deep-learning and discriminant correlation analysis. Int J Image Graph 22(01):2250013

    Article  Google Scholar 

  • Dua M, Chhavi J, Kumar S (2022) LSTM and CNN based ensemble approach for spoof detection task in automatic speaker verification systems. J Ambient Intell Human Comput 13:1985–2000. https://doi.org/10.1007/s12652-021-02960-0

  • Girdhar M, Hong J, Moore J (2023) Cybersecurity of autonomous vehicles: a systematic literature review of adversarial attacks and defense models. IEEE Open J Veh Technol 4:417–437. https://doi.org/10.1109/OJVT.2023.3265363

  • Heidari H, Chalechale A (2022) Biometric authentication using a deep learning approach based on different level fusion of finger knuckle print and fingernail. Expert Syst Appl 191:116278

    Article  Google Scholar 

  • Jomaa RM, Islam MS, Mathkour H et al (2022) A multilayer system to boost the robustness of fingerprint authentication against presentation attacks by fusion with heart-signal. J King Saud Univ-Comput Inf Sci 34(8):5132–5143. https://doi.org/10.1016/j.jksuci.2022.01.004

  • Kocacinar B, Tas B, Akbulut FB et al (2022) A real-time CNN-based lightweight mobile masked face recognition system. IEEE Access 10:63496–63507. https://doi.org/10.1109/ACCESS.2022.3182055

  • Kumar PM (2016) A new human voice recognition system. Asian J Sci Appl Tech 5(2):23–30. https://doi.org/10.51983/ajsat-2016.5.2.931

  • Kumar R, Sarswat M, Ather D et al (2022a) Deformation adjustment with single real signature image for biometric verification using CNN. Comput Intell Neurosci 2022:44060101. https://doi.org/10.1155/2022/4406101

  • Kumar S, Rani S, Jain A et al (2022b) Face spoofing, age, gender and facial expression recognition using advance neural network architecture-based biometric system. Sensors 22(14):5160. https://doi.org/10.3390/s22145160

  • Mazumdar JB, Nirmala SR (2023) Deep learning framework for biometric authentication using retinal images. Comput Methods Biomech Biomed Eng: Imaging Vis 11(3):740–749

    Google Scholar 

  • Mehta J, Talati S, Upadhyay S et al (2023) Regenerating vital facial keypoints for impostor identification from disguised images using CNN. Exp Syst Appl 219:119669. https://doi.org/10.1016/j.eswa.2023.119669

  • Nahar P, Chaudhari NS, Tanwani SK (2022) Fingerprint classification system using CNN. Multimed Tools Appl 81(17):24515–24527

    Article  Google Scholar 

  • Prakash AJ, Patro KK, Hammad M et al (2022) BAED: a secured biometric authentication system using ECG signal based on deep learning techniques. Biocybernetics Biomed Eng 42(4):1081–1093. https://doi.org/10.1016/j.bbe.2022.08.004

  • Priyadarshini I, Sharma R, Bhatt D (2022) Human activity recognition in cyber-physical systems using optimized machine learning techniques. Cluster Comput 26:2199–2215. https://doi.org/10.1007/s10586-022-03662-8

  • Rajakumari K, Punitha P, Lakshmana Kumar R et al (2022) Improvising packet delivery and reducing delay ratio in mobile ad hoc network using neighbor coverage-based topology control algorithm. Int J Commun Syst 35(2):e4260. https://doi.org/10.1002/dac.4260

  • Saba Raoof S, Saleem Durai MA (2022) A comprehensive review on smart health care: applications, paradigms, and challenges with case studies. Contrast Media Mol Imaging 2022:4822235. https://doi.org/10.1155/2022/4822235

  • Sandhya M, Morampudi MK, Pruthweraaj I et al (2023) Multi-instance cancelable iris authentication system using triplet loss for deep learning models. Visual Comput 39(4):1571–1581. https://doi.org/10.1007/s00371-022-02429-x

  • Santhoshkumar SP, Sivaparthipan CB, Prabakar D et al (2013) Secure encryption technique with keying based virtual energy for wireless sensor networks. Int J Adv Res Comput Sci Manage Stud 1(5):139–144

  • Seok CL, Song YD, An BS, Lee EC et al (2023) Photoplethysmogram biometric authentication using a 1D siamese network. Sensors 23(10):4634. https://doi.org/10.3390/s23104634

  • Shi L, Qian C, Guo F (2022) Real-time driving risk assessment using deep learning with XGBoost. Accid Anal Prev 178:106836. https://doi.org/10.1016/j.aap.2022.106836

  • Zhang H, Yang Z (2023) Biometric authentication and correlation analysis based on CNN-SRU hybrid neural network model. Comput Intell Neurosci 2023:8389193. https://doi.org/10.1155/2023/8389193

  • Zhang K, Xu G, Jin YK et al (2023) Palmprint recognition based on gating mechanism and adaptive feature fusion. Front Neurorobotics 17:1203962. https://doi.org/10.3389/fnbot.2023.1203962

  • Zhu G, Al-Qaraghuli Y (2022) AI-assisted authentication: state of the art, taxonomy and future roadmap. arXiv preprint. arXiv:2204.12492. Accessed 25 June 2023

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Authors and Affiliations

  1. Department of Information Technology, Malla Reddy Engineering College for Women, Maisammaguda, Hyderabad, Telangana, India

    Subba Reddy Borra

  2. Department of Electrical and Electronics Engineering, CMR College of Engineering and Technology, Hyderabad, Telangana, India

    B. Premalatha

  3. Department of Information Technology, CMR Technical Campus, Medchal, Hyderabad, Telangana, India

    G. Divya

  4. Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, India

    B. Srinivasarao

  5. Department of Computer Science and Engineering, Kommuri Pratap Reddy Institute of Technology, Ghatkesar, Hyderabad, Telangana, India

    D. Eshwar

  6. Department of Mechanical Engineering, Malla Reddy Engineering College and Management Sciences, Kishtapur, Medchal, Hyderabad, Telangana, India

    V. Bharath Simha Reddy

  7. Department of Artificial Intelligence, SAK Informatics, Hyderabad, Telangana, India

    Pala Mahesh Kumar

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  1. Subba Reddy Borra
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Contributions

Subba Reddy Borra, B. Premalatha, G. Divya, is responsible for designing the framework, analyzing the performance, validating the results, and writing the article. B. Srinivasarao, D. Eshwar, V. Bharath Simha Reddy, Pala Mahesh Kumar, is responsible for collecting the information required for the framework, provision of software, critical review, and administering the process.

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Correspondence to Pala Mahesh Kumar.

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Borra, S.R., Premalatha, B., Divya, G. et al. Deep hashing with multilayer CNN-based biometric authentication for identifying individuals in transportation security. J Transp Secur 17, 4 (2024). https://doi.org/10.1007/s12198-024-00272-w

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