Credit card fraud anomaly detection in mobile cloud service security using Extended Isolation Forest with hyperparameter optimization

The surge in e-commerce has seen an increase in mobile-based credit card transactions, resulting in a sharp escalation of fraud that inflicts substantial financial losses on both consumers and corporations. Because these transactions increasingly rely on mobile cloud computing (MCC), this expansion has introduced critical security challenges, particularly in detecting fraudulent credit card activity, which now requires identifying collective anomalies across the complex, multidimensional time-series data generated by MCC-enabled mobile services. Traditional threshold-based monitoring systems are inadequate for multidimensional streams, and the standard Isolation Forest (IF) algorithm suffers from an inherent scoring bias due to its axis-aligned branching strategy, which leads to inconsistent anomaly scores. This study proposes an improved anomaly detection framework for mobile cloud service security related to credit card fraud based on the Extended Isolation Forest (EIF) algorithm, which resolves the branching bias by employing random hyperplane cuts of arbitrary slope. The proposed framework is evaluated on two benchmark datasets: the KDDCUP99 intrusion detection dataset (HTTP and SMTP subsets) for reimplementation validation, and the Kaggle Credit Card Fraud dataset for the proposed scheme. Results show that the proposed EIF achieves an AUC of 91.05%, a precision of 99.82%, a recall of 95.33%, and an F1-score of 97.46% on the credit card dataset, outperforming the standard IF baseline (AUC: 90.58%, F1: 97.35%). On the KDDCUP99 HTTP subset, the IF achieves a mean AUC of 96.21%, and on the SMTP subset, a mean AUC of 99.00% across four data shuffling runs. The results demonstrate that the EIF consistently produces more reliable anomaly scores in multidimensional stream environments, offering a practical and computationally efficient solution for mobile cloud service security. Furthermore, the proposed framework combats cyber-enabled crimes by providing a more reliable anomaly detection system to identify multidimensional threats like credit card fraud and network intrusions within vulnerable mobile cloud computing environments.

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