Abstract:

The convergence of mobile apps and cloud computing has propelled Mobile Cloud Computing into the spotlight, attracting significant attention from academia and industry alike. Concerns surrounding the security of mobile cloud applications primarily revolve around data integrity, user privacy, and service availability. Taking a preventive approach to address security issues, early detection and isolation of cyber risks in the mobile cloud computing system is essential. Our research introduces a cutting-edge framework that utilizes deep learning to detect cyber-attacks in the mobile cloud environment. The proposed model employs BMO-DBN, a deep belief network (DBN) optimized using the barnacles mating optimizer (BMO) method, for cyber-attack detection. Through empirical evidence, we substantiate that our suggested framework not only distinguishes various types of cyberattacks but also achieves a remarkable level of accuracy

Description:

Cyber Security, Mobile cloud computing, deep learning approach, cyber-attacks detection, deep belief network