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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | A Multi-perspective Fraud Detection Method for Multi- Participant E-commerce Transaction |
| Authors Name: | A.Likhitha , Y.Pallavi , B.Iswarya , J.Madhuri , R.Naveen kumar |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_217329
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| Published Paper Id: | IJNRD2404218 |
| Published In: | Volume 9 Issue 4, April-2024 |
| DOI: | |
| Abstract: | In the realm of e-commerce, where transactions involve multiple participants such as buyers, sellers, and intermediaries, the detection of fraudulent activities presents a significant challenge. To address this issue, our proposed method focuses on a Multi perspective approach aimed at enhancing fraud detection accuracy and efficiency. The first step involves the detection of user behaviors, wherein we leverage various techniques such as behavioral analysis and examination of transaction histories to gain insights into normal user behavior patterns. By understanding typical user interactions within the e-commerce ecosystem, we establish a baseline against which abnormal behaviors can be identified. Subsequently, we delve into the analysis of abnormalities for feature extraction. Utilizing sophisticated anomaly detection algorithms, we scrutinize transaction data to uncover irregular patterns indicative of potentially fraudulent activities. This process allows us to extract important features that serve as key indicators for fraud detection. Finally, we employ an ensemble classification model to implement our fraud detection mechanism, avoiding reliance on a specific algorithm. Instead, we leverage the strengths of ensemble algorithms, such as Random Forest, Gradient Boosting, or Ada Boost. By feeding the extracted features into the ensemble model, we train it to discern between legitimate and fraudulent behaviors in multi-participant e-commerce transactions. Ensemble methods are particularly well-suited for this task due to their ability to handle high-dimensional data and capture complex decision boundaries through the combination of diverse base models. |
| Keywords: | Multiparticipant E-commerce Transactions, Fraud Detection, User Behaviors, Abnormalities Analysis, Ensemble Classification Model, Random Forest, Gradient Boosting, AdaBoost |
| Cite Article: | "A Multi-perspective Fraud Detection Method for Multi- Participant E-commerce Transaction", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 4, page no.b959-b963, April-2024, Available :http://www.ijnrd.org/papers/IJNRD2404218.pdf |
| Downloads: | 00031 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2404218 Registration ID: 217329 Published In: Volume 9 Issue 4, April-2024 DOI (Digital Object Identifier): Page No: b959-b963 Country: Ananthapur, Andhra Pradesh, India Research Area: Computer Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2404218 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2404218 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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