E-commerce Security: An Overview of AI Driven Threat / Anomaly Detection
E-commerce Security: An Overview of AI Driven Threat / Anomaly Detection |
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| © 2025 by IJETT Journal | ||
| Volume-73 Issue-6 |
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| Year of Publication : 2025 | ||
| Author : M. Sakthivanitha, S. Silvia Priscila, Praveen B.M | ||
| DOI : 10.14445/22315381/IJETT-V73I6P114 | ||
How to Cite?
M. Sakthivanitha, S. Silvia Priscila, Praveen B.M, "E-commerce Security: An Overview of AI Driven Threat / Anomaly Detection," International Journal of Engineering Trends and Technology, vol. 73, no. 6, pp.157-172, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I6P114
Abstract
The current marketplaces have changed from physical to online electronic markets (e-commerce) due to substantial advancements in information technology. Governments worldwide, especially those in developing countries, have supported and encouraged smaller and medium-sized businesses to conduct business online. Businesses need to discover a better approach to integrating e-commerce (EC) while maintaining its security as it develops. Resolving the core issue of insufficient security on EC web servers and customers' computer systems is necessary for the rapid expansion of EC. This study aims to conduct a Systematic Literature Review (SLR) to identify the vulnerabilities found in EC platforms. Globally, EC has enhanced consumer convenience and technology. EC has a fraud issue. Platforms and merchants combat fraud to safeguard their clients and companies. One effective technique for spotting odd trends and possible fraud is anomaly detection. The article addresses various methods for putting anomaly detection technology into practice and examines its application in EC fraud detection. Therefore, security concerns and suggested fixes pertaining to EC systems will be discussed in the conclusion. These findings can be used as a starting point for more research on EC security concerns, including suggestions and solutions for a safe EC platform.
Keywords
E-Commerce Security, Artificial Intelligence (AI), Threats, Anomaly Detection, Machine Learning (ML), Deep Learning (DL), Fraud Detection.
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