Enhancing Cloud Computing Security through Hybrid CNN-LSTM and Advanced Data Mining Techniques

The possibilities of data processing and storage have been revolutionized by cloud computing, but it is still open to a number of security risks, such as advanced persistent attacks, illegal access, and data breaches. The inadequacy of current security measures to offer all-encompassing protection frequently calls for the investigation of novel alternatives. By creating a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model and integrating it with cutting-edge data mining techniques, this study seeks to improve cloud computing security. The innovative aspect of this study is how data mining techniques and CNN-LSTM models are used to maximize the benefits of each methodology. The suggested system entails gathering and preprocessing information from network traffic, user activity, and cloud service logs. For sequence forecasting and feature extraction, a hybrid CNN-LSTM model is created. To improve the efficacy of the approach, advanced methods for data mining are combined, such as anomaly detection, association rule mining, and clustering. These approaches are integrated into a coherent cloud security solution through the use of a complete computational environment in the execution. When paired with data mining techniques, the hybrid CNN-LSTM model outperformed conventional approaches in identifying and forecasting security risks. The proposed model outperforms baseline approaches in terms of accuracy, precision, recall, and F1 score, as evidenced by its remarkable training accuracy of 98% and validation accuracy of 96%. Combining cutting-edge data mining methods with hybrid CNN-LSTM models is a viable way to identify and reduce security risks. The results highlight how these cutting-edge approaches can revolutionize cloud security procedures and lay the groundwork for more study and useful use in actual cloud systems.