Deep learning-based steganography framework to enhance ad-hoc cloud security

Deep learning-based steganography framework to enhance ad-hoc cloud security

연구 분야: Strategies

학회: International Journal of Information Technology

The given paper introduces a cloud security model that utilizes the virtual-Berkeley open infrastructure for network computing (V-BOINC) system to address the privacy issues in cloud computing by integrating steganography and deep learning so that data can be made secured in ad-hoc cloud. The major contributions are the design of a two-phase system in which the first phase is to define flexible ad-hoc cloud architecture and the second phase is to design a secure steganography scheme, assisted with deep learning algorithms. The proposed framework provides enhanced data security against a variety of threat vectors compared to conventional systems with low computational cost. The model is empirically verified based on efficiency, reliability, and data integrity. It should be noted that this approach achieves extremely high performance in hiding and transferring encrypted information and images, with good improvements in attack resistance and operation stability. The results show that the proposed system has the highest success rate (99.8%), highest accuracy (99.9%), highest encryption efficiency (99.9%), highest transmission reliability (98%) and highest attack resilience (96%).