PriMera Scientific Engineering (ISSN: 2834-2550)

Literature Review

Volume 9 Issue 1

Hybrid Adaptive Security Model (HASM): Enhancing Network Protection through Cryptographic Innovation

Kondwani Ecclesiastico Mussa* and Pooja Sharma

June 30, 2026

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Abstract

We are becoming more and more dependent on digital communication, which makes protecting data from cyber threats a top priority. Threats from quantum computing, complex cyberattacks, and weaknesses in IoT devices are only a few examples of the modern security issues that traditional cryptographic models and network security protocols frequently fail to handle [1, 2]. Cybercriminals are using vulnerabilities in antiquated security systems more frequently as technology develops, which makes the need for creative defence tactics to grow. The Hybrid Adaptive Security Model (HASM) proposed in this study integrates cutting-edge cryptographic innovations such blockchain-based authentication methods, artificial intelligence (AI)-driven anomaly detection, and quantum-resistant encryption [3, 4]. HASM provides a multi-layered, dynamic defence strategy that adjusts to threats in real time while preserving system confidentiality and integrity. Post-quantum cryptography (PQR) algorithms like New Hope, machine learning methods like Isolation Forest for anomaly detection, and smart contracts for decentralised identity verification are some of the elements of this architecture that we develop and assess. When compared to traditional systems, the testing results demonstrate a significant improvement in threat detection accuracy (96%), increased encryption strength, and decreased susceptibility in authentication processes. According to the survey, there is an increasing demand for a unified cybersecurity framework that uses cutting-edge technologies to safeguard private information in increasingly intricate digital environments. For future applications in industries where security is crucial, HASM provides a solid framework.

Keywords: Post-Quantum Cryptography (PQC); AI Security; Blockchain Authentication; IoT Security; Hybrid Security Framework; HASM

References

  1. Li X, Chen L and Zhang Y. “AI-Driven Adaptive Encryption for Network Security”. IEEE Transactions on Information Forensics and Security (2021).
  2. Parker T and Johnson R. “Decentralized Security with Blockchain: Innovations and Challenges”. Journal of Blockchain Research (2023).
  3. Alharthi M., et al. “Blockchain-Based Encryption for Secure Data Storage in Cloud Computing”. Future Generation Computer Systems (2020).
  4. Nelson J and Carter M. “The Future of Quantum Computing and Cryptographic Security”. Quantum Computing Journal (2022).
  5. Martin K and Chen X. “AI-Powered Cybersecurity: Advancements in Real-Time Threat Detection”. IEEE Transactions on Neural Networks and Learning Systems (2023).
  6. Brown A and Davis L. “Zero-Knowledge Proofs for Data Privacy and Security”. Journal of Cryptographic Research (2024).
  7. Wei W and Chen H. “Enhancing 5G Security through Blockchain-Based Protocols”. IEEE Wireless Communications (2024).
  8. Yang J and Li C. “Zero-Trust Security Framework: Principles and Implementation”. Journal of Cyber Security Technology (2021).
  9. Alharthi M., et al. “Blockchain-Based Encryption for Secure Data Storage in Cloud Computing”. Future Generation Computer Systems (2020).
  10. Li X, Chen L and Zhang Y. “AI-Driven Adaptive Encryption for Network Security”. IEEE Transactions on Information Forensics and Security (2021).
  11. Nelson J and Carter M. “The Future of Quantum Computing and Cryptographic Security”. Quantum Computing Journal (2022).
  12. Parker T and Johnson R. “Decentralized Security with Blockchain: Innovations and Challenges”. Journal of Blockchain Research (2023).
  13. Martin K and Chen X. “AI-Powered Cybersecurity: Advancements in Real-Time Threat Detection”. IEEE Transactions on Neural Networks and Learning Systems (2023).
  14. Brown A and Davis L. “Zero-Knowledge Proofs for Data Privacy and Security”. Journal of Cryptographic Research (2024).
  15. Wei W and Chen H. “Enhancing 5G Security through Blockchain- Based Protocols”. IEEE Wireless Communications (2024).
  16. Yang J and Li C. “Zero-Trust Security Framework: Principles and Implementation”. Journal of Cyber Security Technology (2021).
  17. Huang T, Raza M and Singh A. “Lattice-Based Cryptography: Strengthening Post-Quantum Security Protocols”. Journal of Post- Quantum Computing 15.2 (2023): 113-127.
  18. Singh P and Kumar A. “Federated Learning for AI-Based Threat Detection in Distributed Networks”. International Journal of Network Security 22.1 (2024): 44-58.
  19. Lee D, Park H and Choi S. “Blockchain Smart Contracts for Secure Healthcare Identity Management”. IEEE Access 12 (2024): 5493-5505.
  20. Zhao L and We Y. “Lightweight Encryption Protocols for IoT: Performance and Security Trade-Offs”. Sensors 23.9 (2023): 4122.
  21. Ahmed R, Bakar KA and Noor RM. “Hybrid Security Architectures for Next-Generation Networks: A Systematic Review”. ACM Computing Surveys 56.1 (2024): 1-35.
  22. Thomas M and Patel R. “Privacy-Preserving Authentication Using Zero-Knowledge Proofs in Decentralized Systems”. Journal of Cybersecurity and Privacy 3.4 (2023): 567-580.
  23. Chowdhury S and Rahman M. “Adaptive Blockchain-Based Access Control for IoT Networks”. Internet of Things Journal 11.2 (2024): 1199-1212.
  24. Liu Y and Wang X. “AI-Driven Cyber Threat Detection Systems: A Deep Learning Approach”. IEEE Transactions on Network and Service Management (2024).