International Journal of Computer Science & Network Security

국제컴퓨터통신보호논문지학회 (International Journal of Computer Science & Network Security)
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Enhancing E-commerce Security: A Comprehensive Approach to Real-Time Fraud Detection

  • Sara Alqethami (Department of Information Systems, College of Computer and Information Systems, Umm Al-Qura University) ;
  • Badriah Almutanni (Department of Information Systems, College of Computer and Information Systems, Umm Al-Qura University) ;
  • Walla Aleidarousr (Department of Computer Engineering, College of Computer and Information Systems, Umm Al-Qura University)
  • 투고 : 2024.04.05
  • 발행 : 2024.04.30
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초록

In the era of big data, the growth of e-commerce transactions brings forth both opportunities and risks, including the threat of data theft and fraud. To address these challenges, an automated real-time fraud detection system leveraging machine learning was developed. Four algorithms (Decision Tree, Naïve Bayes, XGBoost, and Neural Network) underwent comparison using a dataset from a clothing website that encompassed both legitimate and fraudulent transactions. The dataset exhibited an imbalance, with 9.3% representing fraud and 90.07% legitimate transactions. Performance evaluation metrics, including Recall, Precision, F1 Score, and AUC ROC, were employed to assess the effectiveness of each algorithm. XGBoost emerged as the top-performing model, achieving an impressive accuracy score of 95.85%. The proposed system proves to be a robust defense mechanism against fraudulent activities in e-commerce, thereby enhancing security and instilling trust in online transactions.

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