Journal article Open Access
Enriching E-Commerce Fraud Detection by using Machine Learning
Veena Malik; S. C. Dharmadhikari
Sponsor(s)
Blue Eyes Intelligence Engineering and Sciences Publication(BEIESP)As there has been a proliferation of the internet platform, it has been increasingly getting affordable for a lot of individuals. The rise has been instrumental in achieving several services including the E-commerce platform. This has led to an unprecedented increase in the amount of fraud that is being committed on this platform. The fraud that is being committed on the E-commerce platforms is very different from the frauds committed on other platforms online. Numerous researches have been performed to combat the evils of credit card frauds and money laundering rings. But there is a severe lack of research on the fraud that is committed on the E-commerce platform. Therefore, this research paper defines an innovative approach for the identification of fraud on E-commerce platforms through the implementation of machine learning approaches. The presented technique utilizes Linear Clustering, Entropy Estimation and Frequent itemset mining in addition to the inclusion of Artificial Neural Networks, Hypergraph formation and Fuzzy classification. The implementation of this system will give more security for E-commerce platform-based transactions by identifying fraudulent activities with better efficiency. The methodology has been tested extensively through rigorous experimentation to evaluate the performance metrics which yielded significantly positive results.
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- Publication date:
- September 30, 2020
- DOI:
-
- Keyword(s):
- Linear Clustering, Entropy Estimation, Frequent Itemset, Hyper graph, Artificial Neural Network, Fuzzy Classification.
- Subject(s):
- Published in:
- International Journal of Recent Technology and Engineering (IJRTE): 9 pp. 140-146 (3).
- Related identifiers:
- Cited by
- 2277-3878 (ISSN) (Journal article)
- License (for files):
- Creative Commons Attribution 4.0 International