Dark Web Financial Fraud Identification Using Mathematical Models in Healthcare Domain

Anand Singh Rajawat - School of Computer Sciences and Engineering, Sandip University, Nashik, 422213, India
S.B. Goyal - Faculty of Information Technology, City University, Petaling Jaya, 46100, Malaysia
Ram Kumar Solanki - School of Computer Sciences and Engineering, Sandip University, Nashik, 422213, India
Amit Gadekar - Sandip Institute of Technology and Research Centre, Sandip University Nashik, 422213, India
Dipak Patil - Sandip Institute of Engineering and Management, Sandip University Nashik, 422213, India


Citation Format:



DOI: http://dx.doi.org/10.62527/joiv.8.1.2600

Abstract


The so-called "dark web" has emerged as the most trustworthy platform for thieves to launch their enterprises. The healthcare industry has become a haven for illegal activities such as the sale of medical gadgets, trafficking in human beings, and the purchase of organs. This is because the sector provides a high level of privacy, which makes it an ideal location for engaging in unlawful operations. In this field of research, linear regression is utilized to uncover previously unknown patterns in customer demand. A vector will be created using a time series of medical equipment purchases to do this. When we look at the data the case firm gave us, we notice that people tend to desire to purchase products in one of three ways. After that, we sort the hospitals into groups according to the course of the trend vector by employing a technique known as "hierarchical clustering," which we apply to the data. According to the research findings, the trend-based clustering method is an excellent way to partition hospitals into subgroups that share similar tendencies. According to our model evaluations, no one model can reliably produce the most accurate forecasts for each cluster when used by itself. Some models can be utilized to make accurate predictions, and these models apply to a wide variety of time series that exhibit various patterns.

Keywords


Dark web; fraud identification; mathematical models; healthcare

Full Text:

PDF

References


Z. Ahmad, S. Rahim, M. Zubair, and J. Abdul-Ghafar, “Artificial intelligence (AI) in medicine, current applications and future role with special emphasis on its potential and promise in pathology: present and future impact, obstacles including costs and acceptance among pathologists, practical and philosophical considerations. A comprehensive review,” Diagn Pathol, vol. 16, no. 1, pp. 24–40, Dec. 2021, doi: 10.1186/s13000-021-01085-4.

Karthikeyan Ramalingam, “USE OF ARTIFICIAL INTELLIGENCE IN HISTOPATHOLOGICAL INTERPRETATION - A MINI REVIEW,” International Journal of Histopathological Interpretation, vol. 12, no. 1, pp. 34–39, Jun. 2023, doi: 10.56501/intjhistopatholinterpret.v12i1.883.

N. Anantrasirichai and D. Bull, “Artificial intelligence in the creative industries: a review,” Artif Intell Rev, vol. 55, no. 1, pp. 589–656, Jan. 2022, doi: 10.1007/s10462-021-10039-7.

B. R. Jung, K.-S. Choi, and C. S. Lee, “Dynamics of Dark Web Financial Marketplaces: An Exploratory Study of Underground Fraud and Scam Business,” CrimRxiv, vol. 5, no. 2, pp. 4–24, Sep. 2022, doi: 10.21428/cb6ab371.dbbe560f.

N. Nguyen et al., “A Proposed Model for Card Fraud Detection Based on CatBoost and Deep Neural Network,” IEEE Access, vol. 10, no. 1, pp. 96852–96861, 2022, doi: 10.1109/ACCESS.2022.3205416.

V. Acin, “Making sense of the dark web,” Computer Fraud & Security, vol. 2019, no. 7, pp. 17–19, Jan. 2019, doi: 10.1016/S1361-3723(19)30075-2.

X. Liu and M. Fan, “Identification and Early Warning of Financial Fraud Risk Based on Bidirectional Long-Short Term Memory Model,” Math Probl Eng, vol. 1, no. 8, pp. 1–8, Jul. 2022, doi: 10.1155/2022/2342312.

E. Wilson, “Disrupting dark web supply chains to protect precious data,” Computer Fraud & Security, vol. 4, no. 1, pp. 6–9, Apr. 2019, doi: 10.1016/S1361-3723(19)30039-9.

L. Tkachenko, E. Andrey, G. Pozdeeva, and V. Romanyuk, “Modern approaches of detecting financial statement fraud,” SHS Web of Conferences, vol. 80, no. 1, pp. 1024–1038, Sep. 2020, doi: 10.1051/shsconf/20208001024.

V. Shpyrko and B. Koval, “Fraud detection models and payment transactions analysis using machine learning,” SHS Web of Conferences, vol. 65, no. 1, pp. 1–7, May 2019, doi: 10.1051/shsconf/20196502002.

H. Wang, Z. Wang, B. Zhang, and J. Zhou, “Information collection for fraud detection in P2P financial market,” in MATEC Web of Conferences, N. Asnafi, Ed., New York: Cornell University, Aug. 2018, pp. 6006–6028. doi: 10.1051/matecconf/201818906006.

M. Zarour et al., “Ensuring data integrity of healthcare information in the era of digital health,” Healthc Technol Lett, vol. 8, no. 3, pp. 66–77, Jun. 2021, doi: 10.1049/htl2.12008.

I. H. Sarker, “Machine Learning: Algorithms, Real-World Applications and Research Directions,” SN Comput Sci, vol. 2, no. 3, pp. 160–181, May 2021, doi: 10.1007/s42979-021-00592-x.

V. Shpyrko and B. Koval, “Fraud detection models and payment transactions analysis using machine learning,” in SHS Web of Conferences, S. Semerikov, V. Soloviev, L. Kibalnyk, O. Chernyak, and H. Danylchuk, Eds., Kyiv: EDP Sciences, May 2019, p. 02002. doi: 10.1051/shsconf/20196502002.

A. Bermudez-Villalva and G. Stringhini, “The shady economy: Understanding the difference in trading activity from underground forums in different layers of the Web,” in 2021 APWG Symposium on Electronic Crime Research (eCrime), Boston: IEEE, Dec. 2021, pp. 1–10. doi: 10.1109/eCrime54498.2021.9738751.

M. Herland, R. A. Bauder, and T. M. Khoshgoftaar, “The effects of class rarity on the evaluation of supervised healthcare fraud detection models,” J Big Data, vol. 6, no. 1, p. 21, Dec. 2019, doi: 10.1186/s40537-019-0181-8.

K. S. Sangher, A. Singh, H. M. Pandey, and V. Kumar, “Towards Safe Cyber Practices: Developing a Proactive Cyber-Threat Intelligence System for Dark Web Forum Content by Identifying Cybercrimes,” Information, vol. 14, no. 6, pp. 349–361, Jun. 2023, doi: 10.3390/info14060349.

Yinhong Shi, “The Rightist Turn in Japanese Politics and Its Implications for China-Japan Relations,” in Research Series on the Chinese Dream and China’s Development Path, 1st ed., vol. 1, Li Yang and Li Peilin, Eds., New York: Springer Science and Business Media LLC, 2021, pp. 171–186.

S. Xu, H. K. Chan, E. Ch’ng, and K. H. Tan, “A comparison of forecasting methods for medical device demand using trend-based clustering scheme,” Journal of Data, Information and Management, vol. 2, no. 2, pp. 85–94, Jun. 2020, doi: 10.1007/s42488-020-00026-y.

H. Shi, Y. Chen, and J.-Y. Hu, “Deep learning on information retrieval using agent flow e-mail reply system for IoT enterprise customer service,” J Ambient Intell Humaniz Comput, vol. 1, no. 1, pp. 1–14, Mar. 2021, doi: 10.1007/s12652-021-02991-7.

D. Kolevski, K. Michael, R. Abbas, and M. Freeman, “Cloud Computing Data Breaches in News Media: Disclosure of Personal and Sensitive Data,” in 2022 IEEE International Symposium on Technology and Society (ISTAS), S. S, K. S, and P. I. A, Eds., Kuala Lumpur: IEEE, Nov. 2022, pp. 1–11. doi: 10.1109/ISTAS55053.2022.10227100.

S. Nazah, S. Huda, J. H. Abawajy, and M. M. Hassan, “An Unsupervised Model for Identifying and Characterizing Dark Web Forums,” IEEE Access, vol. 9, no. 1, pp. 112871–112892, Jan. 2021, doi: 10.1109/ACCESS.2021.3103319.

A. K. Pandey et al., “Key Issues in Healthcare Data Integrity: Analysis and Recommendations,” IEEE Access, vol. 8, no. 1, pp. 40612–40628, Jan. 2020, doi: 10.1109/ACCESS.2020.2976687.

V. Jesus and H. J. Pandit, “Consent Receipts for a Usable and Auditable Web of Personal Data,” IEEE Access, vol. 10, no. 1, pp. 28545–28563, Jan. 2022, doi: 10.1109/ACCESS.2022.3157850.

M. R. Arshad, M. Hussain, H. Tahir, S. Qadir, F. I. Ahmed Memon, and Y. Javed, “Forensic Analysis of Tor Browser on Windows 10 and Android 10 Operating Systems,” IEEE Access, vol. 9, no. 1, pp. 141273–141294, Jan. 2021, doi: 10.1109/ACCESS.2021.3119724.

I. Matloob, S. Khan, H. ur Rahman, and F. Hussain, “Medical Health Benefit Management System for Real-Time Notification of Fraud Using Historical Medical Records,” Applied Sciences, vol. 10, no. 15, pp. 5144–522, Jul. 2020, doi: 10.3390/app10155144.

S. Dalal, B. Seth, M. Radulescu, C. Secara, and C. Tolea, “Predicting Fraud in Financial Payment Services through Optimized Hyper-Parameter-Tuned XGBoost Model,” Mathematics, vol. 10, no. 24, p. 4679, Dec. 2022, doi: 10.3390/math10244679.

A. Al Ali, A. M. Khedr, M. El-Bannany, and S. Kanakkayil, “A Powerful Predicting Model for Financial Statement Fraud Based on Optimized XGBoost Ensemble Learning Technique,” Applied Sciences, vol. 13, no. 4, pp. 2272–241, Feb. 2023, doi: 10.3390/app13042272.

T. Ashfaq et al., “A Machine Learning and Blockchain Based Efficient Fraud Detection Mechanism,” Sensors, vol. 22, no. 19, pp. 7162–722, Sep. 2022, doi: 10.3390/s22197162.

T. K. Shakir, R. Scharif, and M. M. Nasir, “A Proposed Blockchain based System for Secure Data Management of Computer Networks,” Journal of Cybersecurity and Information Management, vol. 11, no. 2, pp. 36–46, Jan. 2023, doi: 10.54216/JCIM.110204.