Open Access

ARTICLE

An AI-Blockchain Hybrid Model to Enhance Security and Trust in Web 4.0

Samer R. Sabbah¹, Mohammad Rasmi Al-Mousa¹, Ala’a Al-Shaikh², Ahmad Al Smadi^3,*, Suhaila Abuowaida⁴, Amina Salhi^5,*, Arij Alfaidi⁶

1 Department of Cyber Security, College of Information Technology, Zarqa University, Zarqa, Jordan
2 Cybersecurity Department, Faculty of Science and Information Technology, Al-Zaytoonah University, Amman, Jordan
3 Department of Computer Science, Faculty of Science and Information Technology, Al-Zaytoonah University, Amman, Jordan
4 Department of Data Science and Artificial Intelligence, Faculty of Prince Al-Hussein Bin Abdallah II for IT, Al al-Bayt University, Mafraq, Jordan
5 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
6 Department of Computer Sciences, University College of Duba, University of Tabuk, Tabuk, Saudi Arabia

* Corresponding Authors: Ahmad Al Smadi. Email: ; Amina Salhi. Email:

(This article belongs to the Special Issue: Next-Generation Cybersecurity: AI, Post-Quantum Cryptography, and Chaotic Innovations)

Computers, Materials & Continua 2026, 88(1), 41 https://doi.org/10.32604/cmc.2026.079241

Received 17 January 2026; Accepted 03 March 2026; Issue published 08 May 2026

Abstract

Web 4.0 platforms introduce intelligent, decentralized agents and real-time interactions that increase both utility and attack surface. This paper presents a comprehensive, reproducible AI blockchain hybrid designed to (1) detect SQL injection attacks at scale using a textual TFIDF + machine-learning pipeline, (2) incorporate reputation signals from a real-world Bitcoin OTC trust dataset to compute a TrustAlert Score (TAS) that prioritizes alerts and guides logging policy, and (3) record privacy-preserving audit digests on blockchain, optionally attested via a zero-knowledge proof (ZKP) pipeline. We evaluate the system on a 148 k SQL corpus and Soc-SignBitcoinOTC reputation data. The detection module achieves high accuracy (0.9797), F1 (0.9807), and ROCAUC (0.9972). TAS effectively separates malicious from benign events (TAS AUC = 0.96) and enables selective on-chain logging to reduce cost. Blockchain benchmarks indicate that local (Ganache) throughput is adequate for batched logging, while public testnet (Goerli) exhibits significantly higher latency and gas usage; ZKP attachments further increase on-chain cost. We discuss practical deployment patterns (digest-only on-chain, Layer2 batching), propose evaluation extensions (transfer learning, adversarial red-teaming), and release reproducible scripts for the community.

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Keywords

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