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Original scientific article

SECNET ARCHITECTURE FOR AI-DRIVEN ACCESS CONTROL AND REAL-TIME THREAT DETECTION IN BLOCKCHAIN SYSTEMS

By
Kummara Sruthi Orcid logo ,
Kummara Sruthi
Contact Kummara Sruthi

Research Scholar, Department of Computer Science and Engineering, Annamacharya University, Rajampeta, India

N. Mallikharjuna Rao Orcid logo
N. Mallikharjuna Rao

Professor, Department of Computer Science and Engineering, Annamacharya University, Rajampeta, India

Abstract

Storage systems must be verifiably secure, and access control must be fine grained and transparent, and must provide audit trails for modern cyber-physical and cloud ecosystems. While blockchain ensures immutability and traceability of data, it does not have the ability to make real-time authorization  decisions, identify abnormal behavior, or facilitate privacy-preserving collaborative data analytics across organizations. This paper introduces SecNet, a comprehensive and layered hybrid architecture that integrates smart-contract-based authorization mechanisms, AI-powered anomaly and threat detection, encrypted off-chain storage, federated learning, and a token-based incentive layer. SecNet is based on a
systematic review of twenty-one recent works from the areas of blockchain access control, federated learning, secure multi-party computation, zero-knowledge proofs, reputation systems, and healthcare data governance, the results of which form four evaluation dimensions: Access Authorization Index (AAI), Security Index (SI), Vulnerability Index (VI), and Latency Index (LI). A synthetic-data testbed is developed with 30 independent simulation runs for each system, which is used for comparison with SecNet and with a Traditional-Blockchain baseline and with a Cloud-Centralized baseline. SecNet improves the Mean Algorithmic Execution Latency by 71.9% over Traditional Blockchain and 66.7% over Cloud-Centralized, the Vulnerability Index by 79.0% over Traditional Blockchain, and the Access Authorization Index and Security Index by 68.3% and 50.2% over Traditional Blockchain baseline, respectively. Overall, the improvements are statistically significant (p < 0.001), according to Welch's ttests, and the individual contributions of AI-based access authorization and smart-contract enforcement can be determined through an ablation study. The results are seen as a repeatable, scalable and quantitatively-based model for trustworthy data sharing in distributed cyber-security ecosystems in the context of SecNet.

References

1.
AccessLevel ← AccessAuthorizationModule.assignRole(UserCredentials, RiskScore).
2.
Aai S, V, Li. Potential attack detected. SecureData ← SecureComputationModule.decrypt(ContractResult).
3.
Si ← Normalize ; Userid, Txid, Accesslevel, Threatscore, Aai S, V, et al. SecureResponse ← GenerateResponse(SecureData).
4.
The cooperating modules are initialized in Algorithm 1 and an initial timestamp is stored for calculating the latency. The first authentication step is fail fast: if credentials are not granted.
5.
Selvarajan S, Srivastava G, Khadidos AO, Khadidos AO, Baza M, Alshehri A, et al. An artificial intelligence lightweight blockchain security model for security and privacy in IIoT systems. Journal of Cloud Computing. 2023;12(1).

Citation

This is an open access article distributed under the  Creative Commons Attribution Non-Commercial License (CC BY-NC) License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 

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Issue 36, 2026
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