Secure Cloud Computing By A dual-Layer Encryption Mechanism

: In today’s rapidly expanding era of the Internet of Things (IoT) and the Industrial Internet of Things (IIoT), the emphasis on robust data security and insightful data interpreta- tion is more pronounced than ever. This research introduces a comprehensive approach to both data protection and predictive analytics, leveraging the diverse dataset TON IoT.csv, sourced from a myriad of IoT and IIoT environments. For data security, a dual-encryption technique incorporating both AES and RSA algorithms is established. Its efficacy is evidenced by a perfect match between the original and decrypted datasets, underscoring the integrity of our encryption process. Concurrently, the study ventures into predictive modeling using a modified Snake Optimization Algorithm (SOA) to streamline hyperparameter selection. This subsequently aids in the development and fine-tuning of an LSTM network, which exhibits remarkable predictive accuracy. Additionally, the paper provides an in-depth examination of various encryption methodologies like elliptic curve cryptography (ECC), Lightweight Cryptography for Cloud computing, and homomorphic encryption, while also emphasizing the nuances of encryption in cloud setups, particularly contrasting server-side with client-side encryption and efficient key management. The insights presented serve as a cornerstone for ensuing research, promising a bright future for advancements in IoT and IIoT data protection and analysis