Anomaly Detection in E-commerce Transactions Using Hybrid Deep Learning Models

• Author(s): Shivam Gupta; Dr. Ujwala Sav
• Paper ID: 1715472
• Page: 2392-2394
• Published Date: 26-03-2026
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 9 March-2026
• 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

Citations

IRE Journals:
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

IEEE:
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

APA:
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

MLA:
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

BibTeX

@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}
}