Improved Mitigation of Security Challenges in Ai Powered Mobile Cloud Application Using Deep Belief Attention Network with Support Linear Regression

Abstract

AI-powered mobile cloud apps raise serious security issues with more sophisticated and flexible attacks targeting at these systems. Since conventional security solutions are mostly reactive and only address issues once they have shown up, they could not be enough in the face of fast shifting attack routes. Driven by artificial intelligence, this effort addresses the desire for a more proactive, predictive approach to find and minimise any security breaches in mobile cloud apps. We propose a new hybrid model integrating a Deep Belief Attention Network (DBAN) for enhanced feature extraction with Support Linear Regression (SLR) for efficient classification of security threats. The DBAN component uses its deep learning architecture to identify complex patterns and dependencies inside the data, therefore enhancing the real-time extraction of relevant information. Concurrently, the SLR model identifies these traits to project potential security breaches before they materialise. Our approach was tested on a large dataset comprising more than 100,000 records containing both benign and malicious activity common of mobile cloud systems. Experimental results demonstrate that the proposed model is rather effective with an accuracy of 96.7%, a precision of 94.8%, a recall of 95.5%, and an F1 score of 95.1%. Furthermore, the model proved to be resilient in lowering false alarms by getting a False Positive Rate (FPR) of 3.2% and a False Negative Rate (FNR) of 2.8%, thereby explicitly identifying actual threats. Our hybrid approach offers a whole solution for enhancing security in mobile cloud apps driven by artificial intelligence since it demonstrates superior performance in detection speed and accuracy than current approaches. This predictive method is particularly suited for dynamic environments where quick threat detection and mitigating are quite important.