A Comprehensive Strategy for Detecting Credit Card Fraud in E-Commerce Utilizing DNS Authentication
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Abstract
E-commerce has transformed global trade, enabling businesses to reach audiences worldwide since the World Wide Web's inception in 1990. Companies like Amazon demonstrate this growth, evolving from a small online bookstore to a retail giant. E-commerce's appeal lies in its global reach, cost-efficiency, and 24/7 availability. However, security challenges, especially credit card fraud, remain significant, causing substantial losses to businesses, particularly small and medium-sized enterprises. Addressing fraud in e-commerce through machine learning techniques is crucial. Techniques such as Logistic Regression, Decision Trees, and Hidden Markov Models each offer unique advantages and limitations for detecting fraud, with some able to operate in real time. These methods help reduce false positives and improve fraud detection, making them integral to secure e-commerce environments. This paper introduces a system that uses disposable domain names and custom DNS servers to detect transaction inconsistencies, thus addressing proxy-based fraud attempts. By generating unique hostnames for each transaction, the system enables real-time monitoring and validation of client transactions. This DNS profiling approach strengthens e-commerce security, reduces financial risks, and enhances trust. The findings underscore the need for advanced fraud detection, contributing to safer online transactions and offering valuable insights for future secure e-commerce systems.
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