SECNET ARCHITECTURE FOR AI-DRIVEN ACCESS CONTROL AND REAL-TIME THREAT DETECTION IN BLOCKCHAIN SYSTEMS
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.
Ling Z, Li Y, Zhao W. Research on consumer segmentation and response prediction model for precision marketing based on clustering and deep learning. Proceedings of the 2025 2nd International Conference on Digital Economy, Blockchain and Artificial Intelligence. ACM; 2025. p. 482–7.
2.
Arunraju Chinnaraju. AI-powered consumer segmentation and targeting: A theoretical framework for precision marketing by autonomous (Agentic) AI. International Journal of Science and Research Archive. 2025;14(2):401–24.
3.
El Koufi N, Belangour A, sadiq M. Toward a decision-making system based on artificial intelligence for precision marketing: A case study of Morocco. Journal of Open Innovation: Technology, Market, and Complexity. 2024;10(1):100250.
4.
Hye A. ARTIFICIAL INTELLIGENCE IN PRODUCT MARKETING: TRANSFORMING CUSTOMER EXPERIENCE AND MARKET SEGMENTATION. ASRC Procedia: Global Perspectives in Science and Scholarship. 2023;03(01):132–59.
5.
Alijoyo FA, Aziz TSA, Omer N, Yusuf N, Kumar MD, Ramesh AVN, et al. Personalized marketing: Leveraging AI for culturally aware segmentation and targeting. Alexandria Engineering Journal. 2025;119:8–21.
6.
Talaat FM, Aljadani A, Alharthi B, Farsi MA, Badawy M, Elhosseini M. A Mathematical Model for Customer Segmentation Leveraging Deep Learning, Explainable AI, and RFM Analysis in Targeted Marketing. Mathematics. 2023;11(18):3930.
7.
Hemalatha R, Amulya G, Lalitha CHSNS. AI-Driven Customer Segmentation for Personalized Marketing. 2024 Second International Conference Computational and Characterization Techniques in Engineering &amp; Sciences (IC3TES). IEEE; 2024. p. 1–5.
8.
Mallikraj SV, Pawar SA, Hole S, Gupta R, Alsailawi HA, Mudhafar M. AI-Powered Customer Segmentation: Enhancing Targeted Marketing Strategies Through Machine Learning. 2025 IEEE 5th International Conference on ICT in Business Industry &amp; Government (ICTBIG). IEEE; 2025. p. 1–5.
9.
Lin J. Application of machine learning in predicting consumer behavior and precision marketing. PLOS One. 2025;20(5):e0321854.
10.
Dr. Razia Bano, Farangis Azim, Zunaira Mahmood, Dr. Asif Sanaullah, Dr. Osama Ali. The Role of Artificial Intelligence in Personalized Marketing: Enhancing Customer Experience, Predictive Targeting, and Brand Engagement. The Critical Review of Social Sciences Studies. 2025;3(2):50–65.
11.
Artificial Intelligence in Digital Marketing Enhancing Personalization and Consumer Engagement. Business & Social Sciences. 2025;3(1):1–9.
12.
Agova P, Valcheva D, Asenov O. The Application of Artificial Intelligence in Business Segmentation. BRAIN Broad Research in Artificial Intelligence and Neuroscience. 2025;16(3):74–85.
13.
Uddin HMM. A Systematic Review of Artificial Intelligence-Based Customer Analytics in Personalized Digital Marketing (2019–2026). American Journal of Data Science and Analytics. 2026;07(03):295–339.
14.
Wu L. Research on customer segmentation and precision marketing strategy based on LRFMC model and K-means algorithm. International Conference on Engineering Management, Computer Applications, and Supply Chain (EMCAS 2025). SPIE; 2026. p. 72.
15.
Fang Z, Wan N. Real-time customer segmentation using big data and cluster analysis in enterprise marketing strategies. International Journal of Information and Communication Technology. 2026;27(51):1–16.
16.
Zhu J. Data Analysis of E-Commerce Precision Marketing System Based on Artificial Intelligence Algorithm. 2023 International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE). IEEE; 2023. p. 1–5.
17.
Kong C. Research on Enterprise Digital Precision Marketing Strategy Based on Big Data. Mathematical Problems in Engineering. 2022;2022:1–9.
18.
Rafi MA. Intelligent Customer Segmentation: A Data- Driven Framework for Targeted Advertising and Digital Marketing Analytics. International Journal of Research Publications in Engineering, Technology and Management. 2022;
19.
Abdullah MS, Hasan R. AI-DRIVEN INSIGHTS FOR PRODUCT MARKETING: ENHANCING CUSTOMER EXPERIENCE AND REFINING MARKET SEGMENTATION. American Journal of Interdisciplinary Studies. 2023;04(04):80–116.
20.
Zhao H. Precision Marketing Strategies Driven by Intelligent Technology. 2025 International Conference on Intelligent Communication Networks and Computational Techniques (ICICNCT). IEEE; 2025. p. 1–6.
The statements, opinions and data contained in the journal are solely those of the individual authors and contributors and not of the publisher and the editor(s). We stay neutral with regard to jurisdictional claims in published maps and institutional affiliations.
