Review on IoT Based Cloud Framework for Secure Data Storage with Integration of Kernel Modifications and AI

The Internet of Things, or IoT, has become a game-changing technology that has increased network traffic dramatically and is expected to link many more devices in the future. A popular technology, cloud computing provides flexible and scalable options for storing and retrieving large volumes of data. However, growing hacking events continue to raise serious concerns about cloud security, impeding its wider use. This study examines an AI-driven and updated kernel-based coding strategy for safe data storage in a cloud environment. Cryptographic algorithms, deep learning applications for IoT security, and kernel-level system-based secure data storage are some of the main strategies covered. The deep learning algorithm called Convolution Neural Network- Binary Multi-objective Enhance of the Gorilla Troops Optimizer-K Nearest Neighbour (CNN-BMEGTO-KNN) outperforms the other methods with an accuracy of 99.8%, while the Kernel-Level Forensic Layers for enhancing real-time evidence analysis (KFREIAN) strategy gets a high throughput value of 247 kbps among the examined methods and 96% of security level is obtained Protocol for Identifiable and Deniable Encryption (PRIDE) cryptographic algorithm.