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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM65416

Secure AI-Blockchain Framework with Supply Chain Integrity and Role-Based Smart Contracts for Real-Time Cybersecurity

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM65416

Volume:

10 |

6 |

Jun 2026

Pages:

1-5

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM65416

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

P Lokesh Kumar

P Lokesh Kumar

Department Of Computer Science

Kuppam Engineering College

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RKM

R Kalai Magal

Department Of Computer Science

Kuppam Engineering College

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Abstract

The increasing sophistication of cyber threats has revealed the significant shortcomings of conventional intrusion detection systems, especially in terms of transparency, auditabil-ity, and resistance to log tampering attacks. In this paper, we present a secure AI blockchain framework that combines convolutional neural network-based intrusion detection with a permissioned blockchain platform to provide tamper-proof cy-bersecurity logging in real-time. The approach improves security through hashing of cryptographic alerts with Merkle tree-based structures, role-based access control in smart contracts, and validation of supply chain integrity through smart contracts and hashing models on the blockchain. The proposed design prevents malicious alert manipulation, insider attacks, and attacks of the artificial intelligence model while ensuring low performance overhead. Our experimental results on the CICIDS2017 dataset show high detection accuracy and acceptable latency. Our design offers enhanced forensic auditability and decentralized trust, making it a scalable solution for future intelligent cybersecurity systems.

KEYWORDS

Artificial Intelligence Blockchain Cybersecurity Intrusion Detection Smart Contracts Supply Chain Security Role-Based Access Control Merkle Tree.

Cite This Article

P Lokesh Kumar, R Kalai Magal, "Secure AI-Blockchain Framework with Supply Chain Integrity and Role-Based Smart Contracts for Real-Time Cybersecurity," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 6, pp. 1-5, Jun 2026. https://doi.org/10.55041/IJSREM65416

Full Article/Conclusion

The Secure AI-Blockchain Cybersecurity Framework of-fers a comprehensive and cutting-edge approach to the most pressing challenges in contemporary cybersecurity. The combination of a 1D CNN-based Intrusion Detection System and a permissioned blockchain offers real-time threat detection and forensic immutability and verifiable governance. The Merkle Tree alert aggregation mechanism is highly effective in overcoming scalability issues by optimizing on-chain storage and transaction processing In addition, the end-to-end supply chain integrity verification system provides complete protection throughout the entire AI model lifecycle, including the prevention of model poi-soning and unauthorized manipulation. This integrated framework offers a solid foundation for a resilient, transparent, and auditable cybersecurity posture that can effectively adapt to the dynamic threat environ-ment the synergy between AI for intelligent threat iden-tification and blockchain for tamper-proof logging and verification represents a paradigm shift towards intelligent threat mitigation and decentralized security.

References

[1]     Z. Ullah, A. Waheed, M. I. Mohmand, S. Basar, M. Zareei, and F. Granda, “AICyber-Chain: Combining AI and Blockchain for Improved Cybersecurity,” IEEE Access, vol. 12, pp. 1–15, Jan. 2024. [2]     S. Goundar and I. Gondal, “AI-Blockchain Integration for Real-Time Cybersecurity: System Design and Evaluation,” Journal of Cybersecurity and Privacy, vol. 5, no. 3, pp. 1–18, Aug. 2025. [3]     A. Aljabri, F. Jemili, and O. Korbaa, “Convolutional neural network for intrusion detection using blockchain technology,” International Journal of Computers and Applications, pp. 1–12, Nov. 2023. [4]     N. R. Rejin Paul, V. Nallarasan, N. Krishnaiah, and L. Gu-ganathan, “Blockchain-integrated intrusion detection system with optimized cosine CNN for enhanced privacy and security in cloud computing,” Information Sciences, vol. 678, pp. 1–20, Dec. 2025. [5]     H. Nandanwar and R. Katarya, “A hybrid Blockchain-Based framework for securing intrusion detection systems in internet of things,” Cluster Computing, vol. 28, pp. 1–19, Jul. 2025. [6]     L. Tightiz, L. M. Dang, and K. W. Park, “AIoT-Blockchain Security for Supply Chain Threats in IEC 61850 Substations Using Informer-Powered Reinforcement Learning,” IEEE Internet of Things Journal, vol. 13, no. 1, pp. 1–14, Nov. 2025. [7]     A. Abubakar Aliyu, J. Liu, and E. Gilliard, “A Decentralized and Self-Adaptive Intrusion Detection Approach Using Continuous Learning and Blockchain Technology,” Journal of Data Science and Intelligent Systems, vol. 9, no. 1, pp. 1–13, Jan. 2024. [8]     A. Kumar, B. Sharma, and A. Noonia, “Secure blockchain based intrusion detection for IoT networks,” Discover Computing, vol. 6, no. 2, pp. 1–16, Oct. 2025. [9]     S. Pandya, “Robust Blockchain and Artificial Intelligence-Based Efficient Techniques for Network Intrusion Detection System,” in Proc. Int. Conf. Integrated Circuits and Communication Systems (ICICACS), Feb. 2025, pp. 1–6. [10]     G. Gurung, G. Bendiab, M. Shiaele, and S. Shiaeles, “CIDS: Collaborative Intrusion Detection System Using Blockchain Tech-nology,” in Proc. IEEE Int. Conf. Cyber Security and Resilience (CSR), 2022, pp. 1–7.

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Publication Date

6/24/2026

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