Advanced Cybersecurity Framework for Detecting Fake Data Using Optimized Feature Selection and Stacked Ensemble Learning

As smart cities continue to generate vast quantities of data, data integrity is increasingly threatened by instances of fraud. Anomalous or fake data deteriorate the process and have impacts on decision-making systems and predictive analytics. Hence, an effective and intelligent fake data detection model was designed by combining an advanced feature selection method with a robust ensemble classification framework. Initially, the raw data are eliminated by performing normalization, feature transformation, and noise filtering that enhances the reliability of the model. The dimensionality issues are mitigated by eliminating redundant features via the proposed Elite Tuning Strategy-Enhanced Polar Bear Optimization algorithm. It simulates the hunting behavior of polar bears, balancing exploration and exploitation features. The proposed Stacking Ensemble-based Random AdaBoost Quadratic Discriminant model leverages the merits of diverse base learners, including AdaBoost, Quadratic Discriminant Analysis, and Random Forest, that classify the feature subset and the integration of prediction processes with a meta-feature vector-processed meta-classifier such as a multilayer perceptron or logistic regression model that predicts the final outcome. This hierarchical architecture validates resilience against noise and improves generalization and prediction accuracy. Thus, the experimental results show that the proposed method outperforms existing approaches in terms of accuracy, precision, and latency, yielding values of 98.78%, 98.75%, and 16 ms, respectively, using the UNSW-NB15 dataset.