Predicting Security Breaches in AI-Powered Mobile Cloud Applications Using Deep Random Forest Algorithm
S. Hassan Abdul Cader, Dr. K. Nirmala · Nanotechnology Perceptions · 2024
This research addresses the need for a predictive approach to detect security breaches in AI-powered mobile cloud applications. We propose a novel approach combining Radial ResNet for advanced feature extraction with Random Forest (RF) for classification. This hybrid model is designed to analyze complex and dynamic datasets in real-time, enhancing the predictive capabilities for identifying potential security threats. Results indicate significant efficacy, with the algorithm achieving high accuracy and sensitivity in predicting security breaches. The Radial ResNet–RF combination achieved a training accuracy of 98.5%, with precision, recall, and F1-score values of 97.8%, 98.2%, and 98.0%, respectively. On testing and validation datasets, the model demonstrated accuracies of 96.2% and 95.5%, respectively.