An Enhanced Hybrid Statistical–Learning Approach for Detecting Fake Feedback in Cloud Environments Using Hierarchical Clustering with Reptile Search Algorithm and Particle Swarm Optimized Extreme Learning Machine
Keywords:
Fake feedback detection, clustering with Reptile Search Algorithm, enhanced empirical analysis method, Particle Swarm Optimization-based Extreme Learning MachineAbstract
The proliferation of fake feedback in cloud environments represents a major challenge in maintaining user trust and ensuring service quality. Accordingly, the development of intelligent approaches for distinguishing genuine feedback from fraudulent instances has become increasingly important. In this study, a hybrid framework based on statistical analysis and machine learning is proposed for the detection of fake feedback. The framework integrates improved hierarchical clustering using the Reptile Search Algorithm (AHC-RSA), the E-EDA statistical model for anomaly filtering, and an Extreme Learning Machine optimized with Particle Swarm Optimization (ELM-PSO). This integrated approach reduces intra-cluster dispersion, enhances data separability, improves prediction accuracy, and accelerates model convergence. Experimental evaluations conducted on the CloudArmor and Epinions datasets demonstrated that the proposed model achieved accuracies of 99.83% and 99.84%, respectively. The results indicate that the proposed model outperforms SVM, ANN, LSTM, and GRU algorithms in terms of accuracy, stability, and convergence speed. On average, the proposed model shows an improvement of approximately 5% to 8% in accuracy and F1-score, along with a significant reduction in MSE and RMSE compared to the best existing methods. Therefore, the proposed framework can be considered an effective and reliable approach for detecting fake feedback and enhancing trust in cloud services.
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Copyright (c) 2026 Hussein Kadhim Almamoori (Author); Golnaz Aghaee Ghazvini; Ali Albu-Rghaif, Fariba Majidi (Author)
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