FINGERPRINTS TO AUTHENTICATE TRANSACTIONS IN CONTACTLESS CARDS
DOI:
https://doi.org/10.25271/sjuoz.2023.11.4.1165Keywords:
Minutiae-based, Contactless card, Iris Biometrics, Security, VerificationAbstract
The contactless bank card is a replica of the old fashion payment methods. The contactless card saves the customer a lot of time and effort because the cardholder can tap the card on the card reader instead of carrying a massive amount of cash or memorizing a long password. The transaction will be done in a few seconds, which is a magnificent technique for a very rush and speedy world like this. However, because the contactless card does not require a PIN or signature, it is vulnerable to different types of attacks, and the card can be used by every single person who has the card, even if they are not the real cardholder. Nevertheless, for each new problem, there is a unique solution. Hence this paper presents an innovative way to overcome this problem by embedding a fingerprint sensor into the contactless card to add an extra level of security by creating a virtual environment giving a contactless card and using a minutiae-based algorithm for fingerprint recognition in this contactless card. The work is evaluated based on accuracy using two metrics, false acceptance rate (FAR) and false rejection rate (FRR), algorithm’s matching time, and transaction time. This work shows a good result regarding the transaction time and the possibility of integrating the fingerprint into the contactless card. It displays how fingerprint image quality and features affect fingerprint authentication results. However, it also shows that minutiae-based techniques are not adequate when the dataset is relatively small and has data with low-quality and/or noisy data.
References
Matyushok, V., Krasavina, V., Berezin, A., & García, J. S. (2021). The global economy in technological transformation conditions: A review of modern trends. Economic Research-Ekonomska Istraživanja, 34(1), 1471-1497.
Aron, J., & Muellbauer, J. (2019). The Economics of Mobile Money: harnessing the transformative power of technology to benefit the global poor. Oxford: Centre for the Study of African Economies.
Kandpal, V., & Mehrotra, R. (2019). Financial Inclusion: The Role Of Fintech And Digital Financial Services In India. Indian Journal of Economics & Business, 19(1), 85-93.
Kang, S.-G., Song, M. S., K. J., Lee, J. W., & Kim, J. (2021). Near-field communication in biomedical applications. Sensors, 21(3), 703.
Ali, M. A., Azad, M. A., Centeno, M. P., Hao, F., & Moorsel, A. v. (2019). Consumer-facing technology fraud: Economics, attack methods and potential solutions. Future Generation Computer Systems, 100, 408-427.
Gerpott, T. J., & Meinert, P. (2018). Termination notice of mobile network operator customers after a tariff switch: An empirical study of postpaid subscribers in Germany. Telecommunications Policy, 42(3).
Zhao, H., Anong, S., & Zhang, L. (2019). Understanding the impact of financial incentives on NFC mobile payment adoption. International Journal of Bank Marketing, 37(5), 1296-1312.
Al-Maliki, O., & Al-Assam, H. (2021). Challenge-response mutual authentication protocol for EMV contactless cards. Computers & Security, 103, 102186.
Klimek, L. (2020). Misuse of contactless payment cards with radio-frequency identification. Masaryk University Journal of Law and Technology, 14(2), 259 - 274.
Kılınç, H., & Vaudenay, S. (2018). Secure contactless payment. Information Security and Privacy: 23rd Australasian Conference, ACISP 2018, Wollongong, NSW, Australia, July 11-13, 2018, Proceedings 23., Springer International Publishing.
Furkan Altınok, K., Peker, A., Tezcan, C., & Temizel, A. (2022). GPU accelerated 3DES encryption. Concurrency and Computation: Practice and Experience, 34(9), 36507.
Al-Maliki, O., & Al-Assam, H. (2022). A tokenization technique for improving the security of EMV contactless cards. Information Security Journal: A Global Perspective, 31(5), 511 - 526.
Akinyokun, N., & Teague, V. (2017). Security and privacy implications of NFC-enabled contactless payment systems. Proceedings of the 12th international conference on availability, reliability and security.
Ramesh, V., Jaunky, V. C., Roopchund, R., & Sigh, O. H. (2019). Customer satisfaction’, loyalty and ‘adoption’of e-banking technology in Mauritius. Embedded Systems and Artificial Intelligence: Proceedings of ESAI 2019 (pp. 861-873). Fez, Morocco: Springer Singapore.
Sportiello, L. (2019). “Internet of Smart Cards”: A pocket attacks scenario. International Journal of Critical Infrastructure Protection, 26, 100302.
Lan, X., Xu, J., Zhang, Z., Chen, X., & Luo, Y. (2023). A systematic security analysis of EMV protocol. Computer Standards & Interfaces, 84, 103700.
Yang, M.-H., Luo, J.-N., Vijayalakshmi, M., & Shalinie, S. M. (2022). Contactless Credit Cards Payment Fraud Protection by Ambient Authentication. Sensors, 22(5), 1989.
El Madhoun, N., Bertin, E., & Pujolle, G. (2018). An overview of the EMV protocol and its security vulnerabilities. 2018 Fourth International Conference on Mobile and Secure Services (MobiSecServ). IEEE.
Akter, S., Chellappan, S., Chakraborty, T., Khan, T. A., Rahman, A., & Al Islam, A. A. (2020). Man-in-the-middle attack on contactless payment over NFC communications: design, implementation, experiments and detection. IEEE Transactions on Dependable and Secure Computing , 18(6), 3012 - 2023.
Goode, A. (2018). Biometrics for banking: best practices and barriers to adoption. Biometric Technology Today, 10, 5 - 7.
Suwald, T., & Rottschäfer, T. (2019). Capacitive fingerprint sensor for contactless payment cards. 2019 26th IEEE International Conference on Electronics, Circuits and Systems (ICECS) (pp. 241 - 245). IEEE.
Yadav, S., & Mathuria, M. (2015). Fingerprint recognition based on minutiae information. International Journal of Computer Applications, 120(10).
Bhargava, N., Kumawat, A., & Bhargava, R. (2015). Fingerprint matching of normalized image based on Euclidean distance. Int. J. Comput. Appl , 120(24), 20 - 23.
Babatunde, I. G. (2015). Fingerprint matching using minutiae-singular points network. International Journal of Signal Processing, Image Processing and Pattern Recognition, 8(2), 375 - 388.
Boujnah, S., Jaballah, S., Khalifa, A. B., & Ammar, M. L. (2018). Person's Identification with Partial Fingerprint Based on a Redefinition of Minutiae Features. 2018 IEEE/ACS 15th International Conference on Computer Systems and Applications (AICCSA) (pp. 1- 5). IEEE.
Agarwal, D., Garima, & Bansal, A. (2021). A utility of ridge contour points in minutiae-based fingerprint matching. Proceedings of International Conference on Computational Intelligence and Data Engineering: ICCIDE 2020. Springer Singapore.
Hambalık, P. M.—A. (2016). Fingerprint recognition system using artificial neural network as feature extractor: design and performance evaluation. Tatra Mt. Math. Publ, 67, 117 - 134.
Zhang, F., Xin, S., & Feng, J. (2019). Combining global and minutia deep features for partial high-resolution fingerprint matching. Pattern Recognition Letters, 119, 139 - 147.
Chowdhury, A., Kirchgasser, S., Uhl, A., & Ross, A. (2020). Can a CNN automatically learn the significance of minutiae points for fingerprint matching? In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, (pp. 351 - 359).
Zukarnain, Z. A., Muneer, A., & Ab Aziz, M. K. (2022). Authentication securing methods for mobile identity: Issues, solutions and challenges. Symmetry, 14(4), 821.
Yang, W., Wang, S., Hu, J., Zheng, G., & Valli, C. (2019). Security and accuracy of fingerprint-based biometrics: A review. Symmetry, 11(2), 141.
Hong, L., Wan, Y., & Jain, A. (1998). Fingerprint image enhancement: algorithm and performance evaluation. IEEE transactions on pattern analysis and machine intelligence , 20(8), 777 - 789.
Patel, M. B., Parikh, S. M., & Patel, A. R. (2019). Performance improvement in preprocessing phase of fingerprint recognition. Information and Communication Technology for Intelligent Systems: Proceedings of ICTIS 2018. 2. Springer Singapore.
Zhang, T. Y., & Suen, C. Y. (1997). A fast parallel algorithm for thinning digital patterns. Communications of the ACM, 27(3), 337 - 343.
Suwarno, S., & Santosa, I. (2019). Simple verification of low-resolution fingerprint using non-minutiae feature. Journal of Physics: Conference Series, 1196(1), 012062.
Kaur, M., Singh, M., Girdhar, A., & Sandhu, P. S. (2008). Fingerprint verification system using minutiae extraction technique. International Journal of Computer and Information Engineering, 2(10), 3405 - 3410.
George, J., & Gladston Raj, S. (2021). Leaf Identification using Harris Corner Detection, SURF Feature and FLANN Matcher. Int. J. Innov. Technol. Explor. Eng, 8(8).
Kurnaz, S., & Mohammed, H. (2020). Secure pin authentication in java smart card using honey encryption. 2020 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA) (pp. 1 - 4). IEEE.
Kaur, N. (2021). A study of biometric identification and verification system. 2021 International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE). IEEE.
Chaudhari, A. S., Lade, S., & Pande, D. S. (2014). Improved Technique for Fingerprint Segmentation. Int. J. Adv. Res. Comput. Sci. Manag. Stud, 2, 402 - 411.
Liu, L. M. (2013). A RFID controller with contactless cards for internet of things. Applied Mechanics and Materials., 336, 2521 - 2524.
Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International journal of computer vision, 60, 91 - 100.
Bounie, D., & Youssouf, C. (2020). Card-sales response to merchant contactless payment acceptance. Journal of Banking & Finance, 119(105938).
Singh, G., Kaushik, D., Handa, H., Kaur, G., Chawla, S. K., & Elngar, A. A. (2021). BioPay: A Secure Payment Gateway through Biometrics. Journal of Cybersecurity and Information Management (JCIM), 7(2), 65 - 76.
Lavadkar, M. A., Thorat, P. K., Kasliwal, A. R., Gadekar, J. S., & Deshmukh, D. P. (n.d.). Fingerprint Biometric Based Online Cashless Payment System. IOSR Journal of Computer Engineering (IOSR-JCE), 27 - 32.
AliBabaee, A., & Broumandnia, A. (2019). Biometric authentication of fingerprint for banking users, using stream cipher algorithm. Journal of Advances in Computer Research, 9(4), 1-17.
Biometric. (2014). Mastercard And Zwipe Launch Fingerprint Payment Card As Alipay Looks To Biometrics. Biometric Technology Today., 11(1).
Mehr Nezhad, M., & Hao, F. (2021). OPay: an Orientation-based Contactless Payment Solution Against Passive Attacks. Annual Computer Security Applications Conference.
Dommaraju, B. T., Kondaveeti, D. S., Katta, S., Devanaboina, V. N., & Cherukupalli, N. L. (2023). Fingerprint Sensor based Biometric Payment Cards. 2023 7th International Conference on Computing Methodologies and Communication (ICCMC). IEEE.
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 soleen J. Ibrahim, Ahmad B. Al-Khalil
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License [CC BY-NC-SA 4.0] that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work, with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online.