Secure key based cloud security utilizing three-way protection with martino homomorphic encryption for preventing unauthorized data access

Cloud computing has transformed data storage and access by providing scalable and on-demand services. Nevertheless, it remains a priority issue to ensure the protection of sensitive data in cloud environments. Several existing security methods has fundamental shortcomings like poor threat prediction features, a failure to process encrypted data securely and high encryption time. To overcome these issues, this study proposes a novel secure key based cloud security utilizing Three-Way Protection with Martino Homomorphic Encryption for preventing unauthorized data access (SKCS-TWP-MHE-PUDA). Initially, the data are collected from Enron Email dataset. Then the input data is given to Reverse Lognormal Kalman Filter (RLKF) for data cleaning and normalization. Next, Koopman Theory Graph Convolutional Network (KTGCN) is used to analyze packet status, predict potential threats and prevent unauthorized cloud access. This real-time intrusion detection mechanism enables early anticipation of malicious activity. Meanwhile, Martino Homomorphic Encryption (MHE) is used to ensure data confidentiality by encrypting cloud-stored data such that only legitimate users decrypt and access it. The three-way security mechanism comprising user registration, intrusion detection and intrusion prevention strengthens overall protection. The performance of the proposed SKCS-TWP-MHE-PUDA method provides 26.68%, 25.75%, and 26.16% higher accuracy 29.08%, 30.70% and 16.26% higher precision when compared with existing techniques: Stochastic Gradient Descent long short-term memory dependent secure encryption approach for cloud data storage and retrieval in cloud computing environs (SGDLSTM-CDS-CCE), Blockchain Key Management: A Solution for Cloud Data Security (AES-BKY-CDS) and deep learning method with cryptographic transformation for enhancing data security in cloud environs (SqueezeNet-DS-CE) respectively.