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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM65416
Volume:
10 |
6 |
Jun 2026
Pages:
1-5
Publication Frequency:
Monthly (12 issues per year)
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.

Department Of Computer Science
Kuppam Engineering College
Department Of Computer Science
Kuppam Engineering College
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
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.
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Publication Date
6/24/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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The authors declare no conflict of interest related to this research work.
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