A Survey and a Credit Card Fraud Detection and Prevention Model using the Decision Tree Algorithm

Authors

  • Abdulaziz Saleh Alraddadi College of Computer Science and Engineering, Taibah University, Saudi Arabia
Volume: 13 | Issue: 4 | Pages: 11505-11510 | August 2023 | https://doi.org/10.48084/etasr.6128

Abstract

Today, many people prefer online payment methods due to the rapid growth in cashless electronic transactions. Credit and debit cards are the most popular electronic payment methods but are prone to fraud due to the nature of their use and the tendency of fraudsters to access their details. This study proposes a theoretical credit fraud detection and prevention model using a Decision Tree Algorithm (DCA). Moreover, a survey questionnaire was used to investigate students' perceptions of credit card fraud incidents. Data were collected from 102 students from different universities and countries around the world. The results showed that 95.9% of the respondents knew how credit/debit card fraud occurs, while 4.1% of them did not. Finally, 81.6% expressed their willingness to use a tool based on the proposed model to prevent or detect credit/debit card fraud incidents.

Keywords:

online payment, credit card fraud, Decision Tree Algorithm (DCA), survey

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References

G. Meško, "On Some Aspects of Cybercrime and Cybervictimization," European Journal of Crime, Criminal Law and Criminal Justice, vol. 26, no. 3, pp. 189–199, Aug. 2018.

A. Al-Dhaqm, W. M. S. Yafooz, S. H. Othman, and A. Ali, "Database Forensics Field and Children Crimes," in Kids Cybersecurity Using Computational Intelligence Techniques, W. M. S. Yafooz, H. Al-Aqrabi, A. Al-Dhaqm, and A. Emara, Eds. Cham, Switzerland: Springer International Publishing, 2023, pp. 81–92.

M. Q. Mohammed et al., "Deep Reinforcement Learning-Based Robotic Grasping in Clutter and Occlusion," Sustainability, vol. 13, no. 24, Jan. 2021, Art. no. 13686.

W. M. S. Yafooz, A. Al-Dhaqm, and A. Alsaeedi, "Detecting Kids Cyberbullying Using Transfer Learning Approach: Transformer Fine-Tuning Models," in Kids Cybersecurity Using Computational Intelligence Techniques, W. M. S. Yafooz, H. Al-Aqrabi, A. Al-Dhaqm, and A. Emara, Eds. Cham, Switzerland: Springer International Publishing, 2023, pp. 255–267.

I. U. Onwuegbuzie, S. A. Razak, I. F. Isnin, A. Al-dhaqm, and N. B. Anuar, "Prioritized Shortest Path Computation Mechanism (PSPCM) for wireless sensor networks," PLOS ONE, vol. 17, no. 3, 2022, Art. no. e0264683.

A. Al-dhaqm, M. Bakhtiari, E. Alobaidi, and A. Saleh, "Studding and Analyzing Wireless Networks Access points," International Journal of Scientific & Engineering Research, vol. 4, no. 1, Jan. 2013.

R. Al-Mugerrn, A. Al-Dhaqm, and S. H. Othman, "A Metamodeling Approach for Structuring and Organizing Cloud Forensics Domain," in 2023 International Conference on Smart Computing and Application (ICSCA), Hail, Saudi Arabia, Oct. 2023, pp. 1–5.

A. A. Zubair et al., "A Cloud Computing-Based Modified Symbiotic Organisms Search Algorithm (AI) for Optimal Task Scheduling," Sensors, vol. 22, no. 4, Jan. 2022, Art. no. 1674.

B. E. Sabir, M. Youssfi, O. Bouattane, and H. Allali, "Towards a New Model to Secure IoT-based Smart Home Mobile Agents using Blockchain Technology," Engineering, Technology & Applied Science Research, vol. 10, no. 2, pp. 5441–5447, Apr. 2020.

M. Saleh et al., "A Metamodeling Approach for IoT Forensic Investigation," Electronics, vol. 12, no. 3, Jan. 2023, Art. no. 524.

A. E. Yahya, A. Gharbi, W. M. S. Yafooz, and A. Al-Dhaqm, "A Novel Hybrid Deep Learning Model for Detecting and Classifying Non-Functional Requirements of Mobile Apps Issues," Electronics, vol. 12, no. 5, Jan. 2023, Art. no. 1258.

K. N. Qureshi et al., "A Blockchain-Based Efficient, Secure and Anonymous Conditional Privacy-Preserving and Authentication Scheme for the Internet of Vehicles," Applied Sciences, vol. 12, no. 1, Jan. 2022, Art. no. 476.

A. Al-dhaqm, "Detection and prevention of malicious activities on RDBMS relational database management systems," International Journal of Scientific & Engineering Research, vol. 3, no. 9, Sep. 2012.

I. U. Onwuegbuzie, S. A. Razak, I. F. Isnin, T. S. J. Darwish, and A. Al-dhaqm, "Optimized backoff scheme for prioritized data in wireless sensor networks: A class of service approach," PLOS ONE, vol. 15, no. 8, 2020, Art. no. e0237154.

S. Abd Razak, N. H. Mohd Nazari, and A. Al-Dhaqm, "Data Anonymization Using Pseudonym System to Preserve Data Privacy," IEEE Access, vol. 8, pp. 43256–43264, 2020.

W. A. H. Altowayti et al., "The Role of Conventional Methods and Artificial Intelligence in the Wastewater Treatment: A Comprehensive Review," Processes, vol. 10, no. 9, Sep. 2022, Art. no. 1832.

M. Rasool, N. A. Ismail, A. Al-Dhaqm, W. M. S. Yafooz, and A. Alsaeedi, "A Novel Approach for Classifying Brain Tumours Combining a SqueezeNet Model with SVM and Fine-Tuning," Electronics, vol. 12, no. 1, Jan. 2023, Art. no. 149.

M. Q. Mohammed et al., "Review of Learning-Based Robotic Manipulation in Cluttered Environments," Sensors, vol. 22, no. 20, Jan. 2022, Art. no. 7938.

I. U. Onwuegbuzie, S. A. Razak, and A. Al-Dhaqm, "Multi-Sink Load-Balancing Mechanism for Wireless Sensor Networks," in 2021 IEEE International Conference on Computing (ICOCO), Kuala Lumpur, Malaysia, Aug. 2021, pp. 140–145.

D. M. Bakhtiari and A. M. R. Al-dhaqm, "Mechanisms to Prevent lose Data," International Journal of Scientific & Engineering Research, vol. 3, no. 12, Dec. 2012.

K. Chaudhary, J. Yadav, and B. Mallick, "A review of Fraud Detection Techniques: Credit Card," International Journal of Computer Applications, vol. 45, no. 1, pp. 39–44, May 2012.

A. A. Alghamdi, "Computerised Information Security Using Texture Based Fuzzy Cryptosystem," Engineering, Technology & Applied Science Research, vol. 8, no. 6, pp. 3598–3602, Dec. 2018.

L. Delamaire, H. A. H. Abdou, and J. Pointon, "Credit card fraud and detection techniques : a review," Banks and Bank Systems, vol. 4, no. 2, pp. 57–68, Jul. 2009.

S. Bagga, A. Goyal, N. Gupta, and A. Goyal, "Credit Card Fraud Detection using Pipeling and Ensemble Learning," Procedia Computer Science, vol. 173, pp. 104–112, Jan. 2020.

V. H. Le, N. Q. Luc, T. T. Dao, and Q. T. Do, "Building an Application that reads Secure Information Stored on the Chip of the Citizen Identity Card in Vietnam," Engineering, Technology & Applied Science Research, vol. 13, no. 1, pp. 10100–10107, Feb. 2023.

P. Save, P. Tiwarekar, K. N., and N. Mahyavanshi, "A Novel Idea for Credit Card Fraud Detection using Decision Tree," International Journal of Computer Applications, vol. 161, no. 13, pp. 6–9, Mar. 2017.

J. Vimala Devi and K. S. Kavitha, "Fraud Detection in Credit Card Transactions by using Classification Algorithms," in 2017 International Conference on Current Trends in Computer, Electrical, Electronics and Communication (CTCEEC), Mysore, India, Sep. 2017, pp. 125–131.

B. Wickramanayake, D. K. Geeganage, C. Ouyang, and Y. Xu, "A Survey of Online Card Payment Fraud Detection using Data Mining-based Methods." arXiv, Nov. 27, 2020.

K. Modi and R. Dayma, "Review on fraud detection methods in credit card transactions," in 2017 International Conference on Intelligent Computing and Control (I2C2), Coimbatore, India, Jun. 2017, pp. 1–5.

V. K. Prasad, "Method and system for detecting fraud in credit card transaction," International Journal of Innovative Research in Computer and Communication Engineering, vol. 1, no. 5, 2013.

S. Yadav and S. Siddartha, "Fraud Detection of Credit Card by Using HMM Model," IMPACT: International Journal of Research in Engineering & Technology, vol. 6, no. 1, Jan. 2018.

S. Xuan, G. Liu, Z. Li, L. Zheng, S. Wang, and C. Jiang, "Random forest for credit card fraud detection," in 2018 IEEE 15th International Conference on Networking, Sensing and Control (ICNSC), Zhuhai, China, Mar. 2018, pp. 1–6.

H. Hormozi, M. K. Akbari, E. Hormozi, and M. S. Javan, "Credit cards fraud detection by negative selection algorithm on hadoop (To reduce the training time)," in The 5th Conference on Information and Knowledge Technology, Shiraz, Iran, Feb. 2013, pp. 40–43.

R. R. Popat and J. Chaudhary, "A Survey on Credit Card Fraud Detection Using Machine Learning," in 2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI), Feb. 2018, pp. 1120–1125.

W. Lovo, "Detecting credit card fraud: An analysis of fraud detection techniques," BSc Thesis, James Madison University, Harrisonburg, VA, USA, 2020.

S. Mittal and S. Tyagi, "Performance Evaluation of Machine Learning Algorithms for Credit Card Fraud Detection," in 2019 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence), Noida, India, Jan. 2019, pp. 320–324.

A. Fawzi, S.-M. Moosavi-Dezfooli, and P. Frossard, "The Robustness of Deep Networks: A Geometrical Perspective," IEEE Signal Processing Magazine, vol. 34, no. 6, pp. 50–62, Aug. 2017.

D. K. M, V. Chadda, and H. Jain, "Credit Card Fraud Detection," International Journal of Advanced Science and Technology, vol. 29, no. 06, pp. 2201–2215, May 2020.

I. Sadgali, N. Sael, and F. Benabbou, "Fraud detection in credit card transaction using neural networks," in Proceedings of the 4th International Conference on Smart City Applications, New York, NY, USA, Jul. 2019, pp. 1–4.

A. Ali et al., "Financial Fraud Detection Based on Machine Learning: A Systematic Literature Review," Applied Sciences, vol. 12, no. 19, Jan. 2022, Art. no. 9637.

A. Ali, S. A. Razak, S. H. Othman, and A. Mohammed, "Extraction of Common Concepts for the Mobile Forensics Domain," in Recent Trends in Information and Communication Technology, Johor Bahru, Malaysia, 2018, pp. 141–154.

A. Ali, S. A. Razak, S. H. Othman, A. Mohammed, and F. Saeed, "A metamodel for mobile forensics investigation domain," PLOS ONE, vol. 12, no. 4, 2017, Art. no. e0176223.

A. Al-Dhaqm et al., "Categorization and Organization of Database Forensic Investigation Processes," IEEE Access, vol. 8, pp. 112846–112858, 2020.

"International Journal of Scientific Research in Computer Science, Engineering and Information Technology," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 3, no. 5, pp. 320–325, 2018.

P. R. Vardhani, Y. I. Priyadarshini, and Y. Narasimhulu, "CNN Data Mining Algorithm for Detecting Credit Card Fraud," in Soft Computing and Medical Bioinformatics, N. B. Muppalaneni, M. Ma, and S. Gurumoorthy, Eds. Singapore: Springer, 2019, pp. 85–93.

R. J. Bolton and D. J. Hall, "Unsupervised Profiling Methods for Fraud Detection," Imperial College, London, UK.

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How to Cite

[1]
A. S. Alraddadi, “A Survey and a Credit Card Fraud Detection and Prevention Model using the Decision Tree Algorithm”, Eng. Technol. Appl. Sci. Res., vol. 13, no. 4, pp. 11505–11510, Aug. 2023.

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