Leveraging ensemble learning for enhanced security in credit card transaction fraudulent within smart cities for cybersecurity challenges

Padhi, Bharat Kumar; Chakravarty, Sujata; Naik, Bighnaraj; Nayak, Soumya Ranjan; Poonia, Ramesh Chandra

doi:10.47974/JDMSC-1978

Open Access Research Article

Leveraging ensemble learning for enhanced security in credit card transaction fraudulent within smart cities for cybersecurity challenges

Bharat Kumar Padhi170506102002@cutm.ac.inDepartment of Computer Science & Engineering Centurion University of Technology & ManagementBhubaneswar, Odisha, 761211, IndiaView full profile → , Sujata Chakravartysujata.chakravarty@cutm.ac.inDepartment of Computer Science & Engineering Centurion University of Technology & ManagementBhubaneswar, Odisha, 761211, IndiaView full profile → , Bighnaraj Naikbnaik_mca@vssut.ac.inDepartment of Computer Application Veer Surendra Sai University of TechnologyBurla, Odisha, 768018, IndiaView full profile → , ^*Soumya Ranjan NayakCorresponding authornayak.soumya17@gmail.comSchool of Computer Engineering KIIT Deemed to be UniversitySchool of Computer Engineering Kalinga Institute of Industrial Technology (Deemed to be University)Bhubaneswar, Odisha, 751024, IndiaView full profile → , Ramesh Chandra Pooniarameshcpoonia@gmail.comDepartment of Computer Science CHRIST (Deemed to be University) Delhi-NCRDepartment of Computer Science CHRIST (Deemed to be University) Ghaziabad, Uttar Pradesh, 201003, IndiaView full profile →

* Corresponding author · click or hover a name for details

pp. 1233–1246Vol. 27Issue 4June 2024DOI: 10.47974/JDMSC-1978 Crossmark XML

Published Online:: 26 Jun 2024
Article type:: Research Article
Language:: EN
Article no.:: JDMSC-1978
Pages:: 1233–1246

Abstract

In the age of digital transactions, credit cards have emerged as a prevalent form of payment in smart cities. However, the surge in online transactions has heightened the challenge of accurately discerning legitimate from fraudulent activities. This paper addresses this crucial concern by introducing a pioneering system for detecting fraudulent credit card transactions, particularly within highly imbalanced datasets, in the realm of cybersecurity. This paper proposes a hybrid model to effectively manage imbalanced data and enhance the detection of fraudulent transactions. This paper emphasizes the efficacy of the hybrid approach in proficiently identifying and mitigating fraudulent activities within highly imbalanced datasets, thereby contributing to the reduction of financial losses for both merchants and customers in smart cities. As cybersecurity in smart cities evolves, this paper underscores the significance of ensemble learning and cross-validation techniques in optimizing credit card transaction analysis and fortifying the security of digital payment systems.

Keywords

Cyber security Smart cities Fraud Fraud detection Machine learning Ensemble learning algorithms

Subject Classifications

Primary 94A60Secondary 68T05

References

[1] Malik, Esraa Faisal, et al. “Credit card fraud detection using a new hybrid machine learning architecture.” Mathematics 10.9 : 1480 (2022).
[2] Taha, Altyeb Altaher, and Sharaf Jameel Malebary. “An intelligent approach to credit card fraud detection using an optimized light gradient boosting machine.” IEEE Access 8 : 25579-25587 (2020).
[3] Carrillo, Fabrizio, et al. “Combining unsupervised and supervised learning in credit card fraud detection.” Information sciences 557 : 317-331 (2021).
[4] Randhawa, Kuldeep, et al. “Credit card fraud detection using AdaBoost and majority voting.” IEEE access 6 : 14277-14284 (2018).
[5] Dal Pozzolo, Andrea, et al. “Credit card fraud detection: a realistic modeling and a novel learning strategy.” IEEE transactions on neural networks and learning systems 29.8 : 3784-3797 (2017).
[6] Raj, S. Benson Edwin, and A. Annie Portia. “Analysis on credit card fraud detection methods.” 2011 International Conference on Computer, Communication and Electrical Technology (ICCCET). IEEE, (2011).
[7] Makki, Sara, et al. “An experimental study with imbalanced classification approaches for credit card fraud detection.” IEEE Access 7 : 93010-93022 (2019).
[8] Chen, Rong-Chang, et al. “Personalized approach based on SVM and ANN for detecting credit card fraud.” 2005 international conference on neural networks and brain. Vol. 2. IEEE (2005).
[9] Prakash, A., and C. Chandrasekar. “An ensemble approach for credit card fraud detection.” International Journal of Computer Applications 59.19 (2012).
[10] Lee, Yuh-Jye, Yi-Ren Yeh, and Yu-Chiang Frank Wang. “Anomaly detection via online oversampling principal component analysis.” IEEE transactions on knowledge and data engineering 25.7 : 1460-1470 (2012).
[11] Mittal, Sangeeta, and Shivani Tyagi. “Performance evaluation of machine learning algorithms for credit card fraud detection.” 2019 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence). IEEE (2019).
[12] Patidar, Raghavendra, and Lokesh Sharma. “Credit card fraud detection using neural network.” International Journal of Soft Computing and Engineering (IJSCE) 1.32-38 (2011).
[13] Ghosh, Sushmito, and Douglas L. Reilly. “Credit card fraud detection with a neural-network.” System Sciences, 1994. Proceedings of the Twenty-Seventh Hawaii International Conference on. Vol. 3. IEEE (1994).
[14] Mishra, Ankit, and Chaitanya Ghorpade. “Credit card fraud detection on the skewed data using various classification and ensemble techniques.” 2018 IEEE International Students’ Conference on Electrical, Electronics and Computer Science (SCEECS). IEEE (2018).
[15] Malini, N., and M. Pushpa. “Analysis on credit card fraud identification techniques based on KNN and outlier detection.” 2017 third international conference on advances in electrical, electronics, information, communication and bio-informatics (AEEICB). IEEE (2017).
[16] Dorronsoro, Jose R., et al. “Neural fraud detection in credit card operations.” IEEE transactions on neural networks 8.4 : 827-834 (1997).
[17] Jiang, Changjun, et al. “Credit card fraud detection: A novel approach using aggregation strategy and feedback mechanism.” IEEE Internet of Things Journal 5.5 : 3637-3647 (2018).
[18] Kumar, Ankit, Pankaj Dadheech, Vijander Singh, Ramesh C. Poonia, and Linesh Raja. “An improved quantum key distribution protocol for verification.” Journal of Discrete Mathematical Sciences and Cryptography 22, no. 4 : 491-498 (2019).
[19] Ryman-Tubb, Nick F., Paul Krause, and Wolfgang Garn. “How Artificial Intelligence and machine learning research impacts payment card fraud detection: A survey and industry benchmark.” Engineering Applications of Artificial Intelligence 76 : 130-157 (2018).
[20] Panigrahi, Suvasini, et al. “Credit card fraud detection: A fusion approach using Dempster–Shafer theory and Bayesian learning.” Information Fusion 10.4 : 354-363 (2009).
[21] Awoyemi, John O., Adebayo O. Adetunmbi, and Samuel A. Oluwadare. “Credit card fraud detection using machine learning techniques: A comparative analysis.” 2017 international conference on computing networking and informatics (ICCNI). IEEE (2017).
[22] Fernández, Alberto, et al. “SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary.” Journal of artificial intelligence research 61 : 863-905 (2018).
[23] Padhi, B. K., S. Chakravarty, and B. N. Biswal. “Anonymized credit card transaction using machine learning techniques.” Advances in Intelligent Computing and Communication: Proceedings of ICAC 2019. Springer Singapore (2020).
[24] Credit Card Fraud Dataset. Accessed: Sep. 4, 2023. [Online]. Available:https://www.kaggle.com/mlg-ulb/creditcardfraud/data.
[25] Rajesh Kumar, E., et al. “Suicidal ideation prediction in twitter data using machine learning techniques.” Journal of Interdisciplinary Mathematics 23.1 : 117-125 (2020).
[26] Nayak, Soumya Ranjan, and Jibitesh Mishra. “A modified triangle box-counting with precision in error fit.” Journal of Information and Optimization Sciences 39.1 : 113-128 (2018).
[27] Nayak, Soumya Ranjan, et al. “A statistical analysis of COVID-19 using Gaussian and probabilistic model.” Journal of Interdisciplinary Mathematics 24.1 : 19-32 (2021).
[28] Roy, Arpita, et al. “Fuzzy rule based intelligent system for user authentication based on user behaviour.” Journal of Discrete Mathematical Sciences and Cryptography 23.2 : 409-417 (2020).

Views: 185Downloads: 6Citations: 0