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Anomaly Detection in E-commerce Transactions Using Hybrid Deep Learning Models
Subject area: Science,Engineering and Technology · Area of research: Deep Learning
DOI: https://doi.org/10.64388/IREV9I9-1715472
Abstract
As online shopping continues to explode, so do the tactics used by fraudsters. The old-school method of using "rule-based" systems essentially a rigid checklist of "if this, then that" is falling behind because modern fraud is constantly evolving. To stay ahead, we’ve developed a hybrid framework that acts like a digital detective, combining two powerful tools: Autoencoders and Isolation Forests.
Keywords
Anomaly Detection, E-commerce, Autoencoder, Isolation Forest, Fraud Detection, Deep Learning
How to cite this paper
Shivam Gupta, Dr. Ujwala Sav "Anomaly Detection in E-commerce Transactions Using Hybrid Deep Learning Models" Iconic Research And Engineering Journals Volume 9 Issue 9 2026 Page 2392-2394 https://doi.org/10.64388/IREV9I9-1715472
Shivam Gupta, Dr. Ujwala Sav "Anomaly Detection in E-commerce Transactions Using Hybrid Deep Learning Models" Iconic Research And Engineering Journals, vol. 9, no. 9, Mar. 2026, doi: https://doi.org/10.64388/IREV9I9-1715472
Shivam Gupta, Dr. Ujwala Sav (2026). Anomaly Detection in E-commerce Transactions Using Hybrid Deep Learning Models. Iconic Research And Engineering Journals, 9(9). doi: https://doi.org/10.64388/IREV9I9-1715472
Shivam Gupta, Dr. Ujwala Sav "Anomaly Detection in E-commerce Transactions Using Hybrid Deep Learning Models" Iconic Research And Engineering Journals, vol. 9, no. 9, Mar. 2026. Crossref, https://doi.org/10.64388/IREV9I9-1715472
@article{1715472,
author = {Shivam Gupta, Dr. Ujwala Sav},
title = {Anomaly Detection in E-commerce Transactions Using Hybrid Deep Learning Models},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {9},
pages = {2392-2394},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1715472.pdf},
abstract = {As online shopping continues to explode, so do the tactics used by fraudsters. The old-school method of using "rule-based" systems essentially a rigid checklist of "if this, then that" is falling behind because modern fraud is constantly evolving. To stay ahead, we’ve developed a hybrid framework that acts like a digital detective, combining two powerful tools: Autoencoders and Isolation Forests.},
keywords = {Anomaly Detection, E-commerce, Autoencoder, Isolation Forest, Fraud Detection, Deep Learning},
month = {March},
doi = {https://doi.org/10.64388/IREV9I9-1715472}
}