Enhancing Cloud Data Security with Machine Learning through the Analysis of Random Forest, Deep Neural Network, and Q-Learning Approaches

Abstract

Abstract— This research outlines the cloud data security when using machine learning techniques – Random Forest, Deep Neural Networks, and Q-Learning to prevent unauthorized data transfers and leaks. The major findings point to the fact that DNN showed a higher level of prediction capabilities in comparison with Random Forest – 95% of overall accuracy as opposed to 92%. It is particularly important to consider AUC-ROC of Random Forest, which is 0.96, making it the most reliable model. However, Q-Learning appears to be less accurate with 88% yet more effective when it comes to a cumulative reward and a policy optimization – features that are vital for a changing environment of cloud servers. The findings of the research make a significant contribution to the field of cloud data security, showing the effectiveness of advanced machine learning models in terms of identifying and mitigating security breaches. This, in turn, creates the opportunity of implementation of these techniques into security frameworks for enhancing the resilience and efficiency of the latter. The recommendations for further research lie in the area of hybrid models creation, in particular, the models that would be able to utilize the positive sides of all three techniques. Moreover, the results would be more generalized with a significantly larger dataset comprising diverse cloud environments and threat situations. Finally, the investigation of the models described in a real-time setting and large cloud-based systems may be suggested for further research, given that these characteristics are essential for an effective practical deployment helping resist emerging threats. Keywords- Cloud Data Security, Machine Learning, Random Forest, Deep Neural Networks, Q-Learning