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AI-QR-THSM: AI-Enhanced Quantum-Resistant Three-Way Hashed Security Model for Secure Load-Balanced Edge-Cloud Environments

International Journal of Electrical and Electronics Engineering
© 2026 by SSRG - IJEEE Journal
Volume 13 Issue 2
Year of Publication : 2026
Authors : Setti Sarika, S. Jhansi Rani
10.14445/23488379/IJEEE-V13I2P119
pdf
How to Cite?

Setti Sarika, S. Jhansi Rani, "AI-QR-THSM: AI-Enhanced Quantum-Resistant Three-Way Hashed Security Model for Secure Load-Balanced Edge-Cloud Environments," SSRG International Journal of Electrical and Electronics Engineering, vol. 13,  no. 2, pp. 239-251, 2026. Crossref, https://doi.org/10.14445/23488379/IJEEE-V13I2P119

Abstract:

The emergence of quantum computing and the exponential growth of edge–cloud infrastructures have created new challenges for secure and efficient data transmission. Traditional cryptographic schemes and static load-balancing mechanisms struggle to provide the required adaptability, scalability, and quantum resilience. To address these challenges, this paper proposes an AI-Enhanced Quantum-Resistant Three-Way Hashed Security Model (AI–QR–THSM) designed for next-generation edge–cloud environments. The proposed model integrates a tri-layered hashing framework combining SHA-3, BLAKE2, and a quantum-resistant hash layer based on SPHINCS+, ensuring high entropy and resistance to post-quantum attacks. The proposed AI-driven RL algorithm not only adaptively orchestrates computational load distribution on heterogeneous nodes to minimize the latency with a balance between throughput and energy consumption, but it also achieves experimentally-evaluated performance up to 42% reduction in computation time, 37% increase in throughput, and 45% more load-balancing efficiency over existing Hybrid Cryptographic models when applied to a simulated edge-cloud testbed using EdgeCloudSim and CloudSim plus. Additionally, the proposed model’s resilience against the DDoS and HYBR attacks increases by 60% during peak-hour traffic conditions. These results suggest that the proposed model is capable of ensuring secure, scalable, and energy-efficient cryptographic performance in current and future distributed systems such as 5G/6G, IoT, and smart cities.

Keywords:

Quantum computing, Edge-cloud, Hashed security model, EdgeCloudSim, CloudSim, Distributed systems.

References:

[1] Mohammed A. Aleisa, Abdullah Abuhussein, and Frederick T. Sheldon, “Access Control in Fog Computing: Challenges and Research Agenda,” IEEE Access, vol. 8, pp. 83986-83999, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Nasser S. Albalawi, “Dynamic Scheduling Strategies for Cloud-Based Load Balancing in Parallel and Distributed Systems,” Journal of Cloud Computing, vol. 14, no. 1, pp. 1-25, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Wen Chen, “Dynamic Edge Loading Balancing with Edge Node Activity Prediction and Accelerating the Model Convergence,” Sensors, vol. 25, no. 5, pp. 1-27, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Nisha Devi et al., “A Systematic Literature Review for Load Balancing and Task Scheduling Techniques in Cloud Computing,” Artificial Intelligence Review, vol. 57, no. 10, pp. 1-63, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Pu Li et al., “Deep Reinforcement Learning for Load Balancing of Edge Servers in IoV,” Mobile Networks and Applications, vol. 27, pp. 1461-1474, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Mohammad Sadegh Aslanpour et al., “Load Balancing for Heterogeneous Serverless Edge Computing: A Performance-Driven and Empirical Approach,” Future Generation Computer Systems, vol. 154, pp. 266-280, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Atul B. Kathole et al., “Novel Load Balancing Mechanism for Cloud Networks using Dilated and Attention-Based Federated Learning with Coati Optimization,” Scientific Reports, vol. 15, no. 1, pp. 1-15, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[8] The SPHINCS+ Team, SPHINCS-α Specification Document, NIST PQC Workshop, 2023. [Online]. Available: https://csrc.nist.gov/csrc/media/Projects/pqc-dig-sig/documents/round-1/spec-files/sphincs-alpha-spec-web.pdf
[9] Zebo Yang et al., “A Survey and Comparison of Post-Quantum and Quantum Blockchains,” IEEE Communications Surveys & Tutorials, vol. 26, no. 2, pp. 967-1002, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[10] NIST Releases First 3 Finalized Post-Quantum Encryption Standards, NIST News, 2024. [Online]. Available: https://www.nist.gov/news-events/news/2024/08/nist-releases-first-3-finalized-post-quantum-encryption-standards
[11] Ahmed A.A. Gad-Elrab et al., “Adaptive Multi-Criteria-Based Load Balancing Technique for Resource Allocation in Fog-Cloud Environments,” arxiv Preprint, pp. 1-20, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Yihong Jin, and Ze Yang, “Scalability Optimization in Cloud-Based AI Inference Services: Real-Time Load Balancing and Automated Scaling,” arXiv Preprint, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Muhammad Abdur Rehman Javaid et al., “Impact of Post Quantum Digital Signatures on Blockchain: Comparative Analysis,” The Asian Bulletin of Big Data Management, vol. 4, no. 1, pp. 121-138, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Asmae Zniti, and Nabih El Ouazzani, “A Comparative Study of Hash Algorithms with the Prospect of Developing a CAN Bus Authentication Technique,” International Journal of Electrical and Computer Engineering Systems, vol. 13, no. 9, pp. 741-1746, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Elif Abanoz, SPHINCS+: the Pinnacle of Quantum-Resistant Hash-Based Digital Signatures, 2025. [Online]. Available: https://medium.com/@elifabanoz/sphincs-the-pinnacle-of-quantum-resistant-hash-based-digital-signatures-347285b637e5
[16] Robert Relyea, “Post-Quantum Cryptography: Hash-Based Signatures,” Red Hat Blog, 2022.
[Google Scholar] [Publisher Link]
[17] The First NIST Post-Quantum Cryptographic Standards, PQShield White Paper, 2022. [Online]. Available: https://pqshield.com/wp-content/uploads/2021/02/PQShield-Quantum-Threat-2-The-First-NIST-Post-Quantum-Cryptographic-Standards-July-2022.pdf
[18] Md Washik Al Azad, and Spyridon Mastorakis, “Deduplicator: When Computation Reuse Meets Load Balancing at the Network Edge,” 2024 IFIP Networking Conference (IFIP Networking), Thessaloniki, Greece, 2024.
[Google Scholar] [Publisher Link]
[19] Marin Vidaković, and Kruno Miličević, “Performance and Applicability of Post-Quantum Digital Signature Schemes in Resource-Constrained Systems,” Algorithms, vol. 16, no. 11, pp. 1-18, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Kavish Chawla, “Reinforcement Learning-Based Adaptive Load Balancing for Dynamic Cloud Environments,” arXiv Preprint, pp. 1-6, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Ahmed Hazim Alhilali, and Ahmadreza Montazerolghaem, “Artificial Intelligence based Load Balancing in SDN: A Comprehensive Survey,” Internet of Things, vol. 22, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Haythem Hayouni, “Adaptive Post-Quantum Security Framework for Wireless Sensor Networks using Lightweight Cryptography and Context-Aware Key Management,” The Journal of Supercomputing, vol. 81, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Hina Hashmi et al., Edge-Enabled AI Framework for Real-Time Anomaly Detection in Industrial IoT Systems, Artificial Intelligence and Sustainable Innovation, 1st ed., CRC Press, 2026.
[Google Scholar] [Publisher Link]
[24] Rodrigo Roman, Javier Lopez, and Masahiro Mambo, “Mobile Edge Computing, Fog et al.: A Survey and Analysis of Security Threats and Challenges,” Future Generation Computer Systems, vol. 78, part 2, pp. 680-698, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Zied Guitouni, Noeman Ammar, and Mohsen Machhout, “An Efficient Hardware Implementation of SHA-3 using 3D Cellular Automata for Secure Wireless Sensor Networks,” International Journal of Information Security, vol. 24, 2025.
[CrossRef] [Google Scholar] [Publisher Link]