THREAT MODEL FOR PAYMENT SYSTEMS
Agzamova Mohinabonu , PhD Student Of Tashkent University Of Information Technologies Named After Muhammad Al-Khwarizmi, Tashkent, UzbekistanAbstract
Payment systems are the backbone of modern economies, and their security is a critical aspect of maintaining user trust and safeguarding financial data. In this context, the development of threat models plays a central role in ensuring the protection of these systems from attacks. One of the most effective methodologies for threat modeling is STRIDE, which helps to structure and systematize the analysis of risks and threats. By utilizing the STRIDE model and incorporating a customized threat model for payment systems, the security of authentication processes and transaction handling can be significantly enhanced.
Keywords
Payment systems, threat modeling, authentication
References
Chenqian Yan, Yuge Zhang, Quanlu Zhang, Yaming Yang, Xinyang Jiang, Yuqing Yang, Baoyuan Wang. Privacy-preserving Online AutoML for Domain-Specific Face Detection. URL: https://openaccess.thecvf.com/content/CVPR2022/papers/Yan_Privacy-Preserving_Online_AutoML_for_Domain-Specific_Face_Detection_CVPR_2022_paper.pdf
Yang Liu, Fei Wang, Jiankang Deng, Zhipeng Zhou, Baigui Sun, Hao Li. MogFace: Towards a Deeper Appreciation on Face Detection. URL: https://openaccess.thecvf.com/content/CVPR2022/papers/Liu_MogFace_Towards_a_Deeper_Appreciation_on_Face_Detection_CVPR_2022_paper.pdf
Roberto Pecoraro, Valerio Basile, Viviana Bono, Sara Gallo. Local Multi-Head Channel Self-Attention for Facial Expression Recognition. URL: https://arxiv.org/pdf/2111.07224v2.pdf
Kai Wang, Xiaojiang Peng, Jianfei Yang, Debin Meng, Yu Qiao. Region Attention Networks for Pose and Occlusion Robust Facial Expression Recognition. URL: https://arxiv.org/pdf/1905.04075v2.pdf
Andrey V. Savchenko. Facial expression and attributes recognition based on multi-task learning of lightweight neural networks. URL: https://ieeexplore.ieee.org/abstract/document/9582508/authors#authors
Minchul Kim, Anil K. Jain, Xiaoming Liu. AdaFace: Quality Adaptive Margin for Face Recognition. URL: https://arxiv.org/pdf/2204.00964.pdf
M.Sh.Agzamova, A.G.Nuriddionova., Sh.R.Gulomov: Settings firewalls to implement special filtering mode. XIII INTERNATIONAL SCIENTIFIC AND PRACTICAL CONFERENCE «INTERNATIONAL SCIENTIFIC REVIEW OF THE PROBLEMS AND PROSPECTS OF MODERN SCIENCE AND EDUCATION» Chicago. USA 21-22 APRIL 2016. № 5 (15), p.34-39
M.Sh.Agzamova, A.G.Nuriddionova., A.Mamathanov: Mechanisms of provision of information security as a factor of economic security of small and private business. International Scientific and Practical Conference “WORLD SCIENCE” Proceedings of the IInd International Scientific and Practical Conference Dubai, UAE "Topical researches of the World Science № 7(11), Vol.1, July 2016 p.33-34.
Tashev, K., Durdona, I., Mokhinabonu, A./Comparative performance analysis the Aho-Corasick algorithm for developing a network detection system// 2022 International Conference on Information Science and Communications Technologies, ICISCT 2022
M.Sh.Agzamova, S.G.Svetunkov, A.G.Nuriddionova.: Big Data Simulation for Demand Forecasting in Retail Logistics. Algorithms and Solutions Based on Computer Technology. Lecture Notes in Networks and Systems, vol 387. Springer, Cham. Conference paper, First Online: 04 May 2022, pp 137–147 https://doi.org/10.1007/978-3-030-93872-7_12
Y. Yorozu, M. Hirano, K. Oka, and Y. Tagawa, “Electron spectroscopy studies on magneto-optical media and plastic substrate interface,” IEEE Transl. J. Magn. Japan, vol. 2, pp. 740–741, August 1987 [Digests 9th Annual Conf. Magnetics Japan, p. 301, 1982].
Taigman, Y., Yang, M., Ranzato, M. A., & Wolf, L. (2014). DeepFace: Closing the Gap to Human-Level Performance in Face Verification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 1701-1708).
Schroff, F., Kalenichenko, D., & Philbin, J. (2015). FaceNet: A Unified Embedding for Face Recognition and Clustering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 815-823).
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