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

IF 8.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Swarm and Evolutionary Computation Pub Date : 2025-08-25 DOI:10.1016/j.swevo.2025.102131

Ganji Ramanjaiah , Tummala Srinivasa Ravi Kiran , Ampalam Srisaila , Annemneedi Lakshmanarao , Komanduri Venkata Sesha Sai Ramakrishna , Katakam Venkateswara Rao

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引用次数: 0

Abstract

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.

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利用martino同态加密的三向保护来防止未经授权的数据访问的基于密钥的安全云安全

云计算通过提供可扩展和按需服务，改变了数据存储和访问方式。然而，确保云环境中敏感数据的保护仍然是一个优先问题。现有的几种安全方法存在威胁预测功能差、加密数据处理不安全、加密时间长等根本缺陷。为了克服这些问题，本研究提出了一种新的基于安全密钥的云安全，利用Martino同态加密的三向保护来防止未经授权的数据访问（skcs - twp - mhea - puda）。最初，数据是从安然电子邮件数据集收集的。然后将输入数据交给逆对数正态卡尔曼滤波器（RLKF）进行数据清洗和归一化。接下来，使用库普曼理论图卷积网络（KTGCN）分析数据包状态，预测潜在威胁并防止未经授权的云访问。这种实时入侵检测机制支持对恶意活动的早期预测。同时，Martino同态加密（MHE）通过加密云存储的数据来确保数据的机密性，这样只有合法用户才能解密和访问它。用户注册、入侵检测和入侵防御三方面的安全机制加强了整体防护。与现有技术相比，所提出的skcs - twp - mhea - puda方法的准确率分别提高了26.68%、25.75%和26.16%，分别提高了29.08%、30.70%和16.26%。针对云计算环境下云数据存储和检索的随机梯度下降长短期记忆依赖安全加密方法（SGDLSTM-CDS-CCE），区块链密钥管理：云数据安全解决方案（aes - bkey - cds）和增强云环境下数据安全的加密转换深度学习方法（SqueezeNet-DS-CE）。

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来源期刊

Swarm and Evolutionary Computation COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, THEORY & METHODS

CiteScore

16.00

自引率

12.00%

发文量

169

期刊介绍： Swarm and Evolutionary Computation is a pioneering peer-reviewed journal focused on the latest research and advancements in nature-inspired intelligent computation using swarm and evolutionary algorithms. It covers theoretical, experimental, and practical aspects of these paradigms and their hybrids, promoting interdisciplinary research. The journal prioritizes the publication of high-quality, original articles that push the boundaries of evolutionary computation and swarm intelligence. Additionally, it welcomes survey papers on current topics and novel applications. Topics of interest include but are not limited to: Genetic Algorithms, and Genetic Programming, Evolution Strategies, and Evolutionary Programming, Differential Evolution, Artificial Immune Systems, Particle Swarms, Ant Colony, Bacterial Foraging, Artificial Bees, Fireflies Algorithm, Harmony Search, Artificial Life, Digital Organisms, Estimation of Distribution Algorithms, Stochastic Diffusion Search, Quantum Computing, Nano Computing, Membrane Computing, Human-centric Computing, Hybridization of Algorithms, Memetic Computing, Autonomic Computing, Self-organizing systems, Combinatorial, Discrete, Binary, Constrained, Multi-objective, Multi-modal, Dynamic, and Large-scale Optimization.