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

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.