Predictive Technique Of Security Data Breaches In Ai Powered Mobile Cloud Application Using Deep Random Forest Algorithm

Abstract

With the rapid integration of artificial intelligence (AI) in mobile cloud applications, ensuring robust security mechanisms is vital to safeguard sensitive user data. The proliferation of AI technologies in mobile cloud applications has brought unprecedented efficiency and convenience, accompanied by an escalating risk of security breaches. As the threat landscape evolves, traditional security measures fall short in providing comprehensive protection. This research recognizes the critical need for a predictive approach to security data breaches in AI-powered mobile cloud applications. Existing security frameworks often lack the adaptability to detect and pre-emptively address emerging threats specific to AI-enhanced mobile cloud environments. This study employs the Deep Random Forest Algorithm, an advanced machine learning technique known for its ability to handle complex and dynamic datasets. The algorithm combines the power of deep learning with the versatility of random forest classifiers to predict security breaches in real-time. The results demonstrate the efficacy of the proposed Deep Random Forest Algorithm in predicting and mitigating security breaches in AI-powered mobile cloud applications. The model exhibits high accuracy and sensitivity, showcasing its potential to enhance the security posture of mobile cloud ecosystems