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

Shivam Gupta; Dr. Ujwala Sav

doi:10.64388/IREV9I9-1715472

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

Shivam Gupta Dr. Ujwala Sav

Subject area: Science,Engineering and Technology · Area of research: Deep Learning

DOI: 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

References

[1] Liu, F. T., Ting, K. M., & Zhou, Z. H., “Isolation Forest,” IEEE, 2008.

[2] Goodfellow, I., Bengio, Y., & Courville, A., Deep Learning, MIT Press, 2016.

[3] Chandola, V., Banerjee, A., & Kumar, V., “Anomaly Detection: A Survey,” ACM Computing Surveys, 2009.

[4] Aggarwal, C. C., Outlier Analysis, Springer, 2017.

[5] Chalapathy, R., & Chawla, S., “Deep Learning for Anomaly Detection,” arXiv, 2019

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}
  }