Security Edge Cloud Integration of Holographic Consumer IoT: Big Data Encryption and Real-Time Threat Detection

Expanding digital technologies, holographic consumer IoT devices generating 3D immersive content, and massive multidimensional data streams that require secure and efficient management solutions are on the rise. In this research, we propose a Security Edge-Cloud Integration framework combining big-data encryption to achieve real-time threat detection for holographic IoT systems. Holographic IoT devices generate streaming data collected in real time at the edge layer. The edge sensitive holographic data is secured using Security-enriched Elliptic Curve Cryptography (SECC), ensuring confidentiality, continuous authentication, and integrity at low computational cost prior to sending it to the cloud for data lower-cost storage and analytics. Furthermore, to achieve proactive security, an Adaptive Squill Search-driven Regularized Random Forest (ASS-RRF) model is deployed to monitor the segmented edge stream data in real time, enabling detection of anomalies, potential cyber-time attacks, and unauthorized access before the data reaches the cloud. The experimental results of the components demonstrated that the framework showed improved performance in encryption-decryption efficiency and lower computational costs, and was assessed and shown to be effective in detecting using metrics of accuracy (99.74%), recall (99.65%), precision (99.61%) and F1-score (99.68%) relative to existing detection methods. This integrated approach demonstrated the feasibility of secure, scalable, and intelligence edge-cloud solutions for contemporary holographic consumer IoT systems, providing an innovative foundation for secure next-generation IoT solutions.