IoT-Enabled Secure and Scalable Cloud Architecture for Multi-User Systems: A Hybrid Post-Quantum Cryptographic and Blockchain-Based Approach Toward a Trustworthy Cloud Computing

Abstract

Cloud computing has revolutionized organizational operations by providing convenient, on-demand access to resources. The emergence of the Internet of Things (IoT) has introduced a new paradigm for collaborative computing, leveraging sensors and devices that generate and process vast amounts of data, thereby resulting in challenges related to scalability and security, making the significance of conventional security methods even more pronounced. Consequently, in this paper, we propose a novel Scalable and Secure Cloud Architecture (SSCA) that integrates IoT and cryptographic techniques, aiming to develop scalable and trustworthy cloud systems, thus enabling multi-user systems and facilitating simultaneous access to cloud resources by multiple users. The design adopts a decentralized approach, utilizing multiple cloud nodes to handle user requests efficiently and incorporates Multicast and Broadcast Rekeying Algorithm (MBRA) to ensure the privacy and confidentiality of user information, utilizing a hybrid cryptosystem that combines MBRA, Post Quantum Cryptography (PQC) and blockchain technology. Leveraging IoT devices, the architecture gathers data from distributed sensing resources and ensures the security of collected information through robust MBRA-PQC encryption algorithms, while the blockchain ensures that the confidential data is stored in distributed and immutable records. The proposed approach is applied to several datasets and the effectiveness is validated through various performance metrics, including response time, throughput, scalability, security, and reliability. The results highlight the effectiveness of the proposed SSCA, showcasing a notable reduction in response time by 1.67 seconds and 0.97 seconds for 250 and 1000 devices, respectively, in comparison to the MHE-IS-CPMT. Likewise, SSCA demonstrated significant improvements in the AUC values, exhibiting enhancements of 6.30%, 6.90%, 7.60%, and 7.30% at the 25-user level, and impressive gains of 5.20%, 9.30%, 11.50%, and 15.40% at the 50-user level when compared to the MHE-IS-CPMT, EAM, SCSS, and SHCEF models, respectively.

A model study of teaching method reform of computer laboratory course integrating internet of things technology

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Hybrid key management WSN protocol to enhance network performance using ML Techniques for IoT application in cloud Environment

“…Moreover, incorporating machine learning (ML) techniques further augments these protocols, enabling adaptive and intelligent management of cryptographic keys in WSNs deployed in cloud-based IoT applications. This essay explores the innovative fusion of hybrid key management and ML techniques to enhance network performance [1] [2]and security in IoT applications [3] deployed in the cloud.…”