AI Security in Contactless Payments and Education: A Review

Authors

  • Ahmed Hamed Department of Computer Science, Faculty of Computers and Information, Damanhour University, 22511, Damanhour
  • Yojna Bansal Plekhanov Russian University for Economics in Dubai, Dubai
  • Mostafa Mohamad Zayed University, College of Interdisciplinary Studies, Abu Dhabi
  • Angel Jimenez-Aranda Centre for Sustainable Innovation Salford Business School, The University of Salford, Manchester
  • Tarek Gaber School of Science Engineering and Environment, The University of Salford, Manchester

DOI:

https://doi.org/10.16920/jeet/2025/v39is1/25130

Keywords:

Credit Cards, Adversarial ML, NFC Security, Fraud Detection, Cybersecurity Education, Edge AI Defenses

Abstract

In today’s digital economy, contactless credit cards have become central to financial transactions, offering speed and convenience through technologies such as Near Field Communication (NFC), embedded Wi-Fi, and mobile wallets. However, this shift has also expanded the cybersecurity attack surface. While artificial intelligence (AI)-driven fraud detection has advanced, emerging threats—particularly adversarial machine learning and ransomware targeting card infrastructure—remain underexplored in academic and industrial research. This survey examines the evolving security landscape of contactless credit cards, focusing on AI threats and corresponding defenses. It identifies vulnerabilities in NFC protocols and shows how adversarial examples can bypass traditional fraud detection. The study also explores the potential for ransomware and real-time attacks that exploit digital card systems. In parallel, it evaluates AI-based defensive frameworks, outlining their strengths, limitations, and feasibility in resource-constrained environments. Beyond technical insights, this work highlights the value of integrating such real-world challenges into computer engineering education. By linking AI security with embedded systems, protocols, and threat modeling, it proposes a curriculum framework to prepare students for modern cybersecurity demands. Finally, the paper identifies key research gaps and suggests future directions, including AI solutions to secure contactless payment ecosystems.

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Published

2025-09-10

How to Cite

Hamed, A., Bansal, Y., Mohamad, M., Jimenez-Aranda, A., & Gaber, T. (2025). AI Security in Contactless Payments and Education: A Review. Journal of Engineering Education Transformations, 39(is1), 20–25. https://doi.org/10.16920/jeet/2025/v39is1/25130

Issue

Volume 39, Special Issue 1, September 2025

Section

Research Article
Crossref
Scopus
Google Scholar
Europe PMC

References

Ahamad, S. S. (2021). A novel nfc-based secure protocol for merchant transactions. IEEE Access, 10, 1905–1920.

Balasubramanian, K., & Ghadimi, S. (2022). Zeroth- order nonconvex stochastic optimization: Handling constraints, high dimensionality, and saddle points. Foun- dations of Computational Mathematics, 22(1), 35–76.

Esenogho, E., Mienye, I. D., Swart, T. G., Aruleba, K., & Obaido, G. (2022). A neural network ensemble with feature engineering for improved credit card fraud detection. IEEE access, 10, 16400–16407.

Gangwal, A., Paliwal, A., & Conti, M. (2024). Deauthentication using ambient light sensor. IEEE Access, 12, 28225–28234.

Huang, R., Wei, C., Wang, B., Yang, J., Xu, X., Wu, S., & Huang, S. (2022). Well performance prediction based on long short-term memory (lstm) neural network. Journal of Petroleum Science and Engineering, 208, 109686.

Katagiri, N. (2024). From prepaid cards to bitcoin: How did ransomware hackers adopt cryptocurrencies? Journal of Cyber Policy, 9(2), 239–255.

Kulkarni, R. (2021). Near field communication (nfc) technology and its application. In Techno-societal 2020: Proceedings of the 3rd international conference on ad- vanced technologies for societal applications— volume 1 (pp. 745–751).

Kumar, N., Vimal, S., Kayathwal, K., & Dhama, G. (2021). Evolutionary adversarial attacks on payment systems. In 2021 20th ieee international conference on machine learning and applications (icmla) (pp. 813– 818).

Malatji, M. (2023). Management of enterprise cyber security: A review of iso/iec 27001: 2022. In 2023 international conference on cyber management and engineering (cy- maen) (pp. 117–122).

Mienye, I. D., & Jere, N. (2024). Deep learning for credit card fraud detection: A review of algorithms, challenges, and solutions. IEEE Access.

NISHMA, B., VENANKA, S., BANTU, V. K., MAMIDI, S. K., & MAMINDLA, R. (2024). Credit card fraud detection using state-of-the-art machine learning and deep learning algorithms. International Journal of HRM and Organizational Behavior, 12(2), 169–180.

Njebiu, V., Kimwele, M., & Rimiru, R. (2021). Secure contactless mobile payment system. In 2021 ieee latin- american conference on communications (latincom) (pp. 1–6).

Parameswaran, S., Gogia, Y., Williams, M. J., Nayak, R., Ashfaq, A., Monica, L., & et al. (2024). Cardless society: Assessing the role of cardless atms in shaping the future of financial transactions. In 2024 interna- tional conference on trends in quantum computing and emerging business technologies (pp. i–v).

Tafti, F. S. M., Mohammadi, S., & Babagoli, M. (2021). A new nfc mobile payment protocol using improved gsm based authentication. Journal of Information Security and Applications, 62, 102997.

Tsai, M.-Y., Cho, H.-H., Yu, C.-M., Chang, Y.-C., & Chao, H.C. (2024). Effective adversarial examples identification of credit card transactions. IEEE Intelligent Systems.

Yang, M.-H., Hsu, Y.-S., & Hsu, H.-C. (2025). Enhanced emv security: Preventing credit card fraud from a distance. IEEE Access.

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.

Zhao, X., Wang, L., Zhang, Y., Han, X., Deveci, M., & Parmar, M. (2024). A review of convolutional neural networks in computer vision. Artificial Intelligence Review, 57(4), 99.

Zhong, Y., & Moon, H.-C. (2022). Investigating customer behavior of using contactless payment in china: A comparative study of facial recognition payment and mobile qr-code payment. Sustainability, 14(12), 7150.

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