AI–Blockchain Integration for Real-Time Cyber Security - System Design and Evaluation

This paper presents the design, development, and thorough evaluation of a novel network security prototype that integrates Artificial Intelligence (AI) and blockchain technology to significantly enhance cyber security. As AI becomes increasingly embedded in cybersecurity solutions, ensuring the provenance, accountability, and integrity of AI-generated decisions has emerged as a critical challenge. Without reliable logging mechanisms, AI models remain vulnerable to adversarial manipulation and pose significant risks to critical security infrastructure. To address this, our research combines a state-of-the-art Convolutional Neural Network (CNN)-based threat detection module with a permissioned Ethereum-compatible blockchain. A custom-designed Solidity smart contract ensures secure, structured storage of comprehensive AI model metadata, while interactions with the blockchain are seamlessly managed through a lightweight Flask-based REST API. Each recorded transaction generates a unique cryptographic fingerprint, providing robust evidence for audits and forensic analyses. We evaluated the system's effectiveness through rigorous experimentation on a controlled test network, confirming immutability, traceability, and verifiable integrity of all logged metadata entries. Results demonstrated significant improvements in anomaly detection accuracy, reduced false-positive rates, and ensured real-time responsiveness essential for effective intrusion prevention. Despite controlled-environment limitations, such as transaction latency and blockchain-related operational costs, our prototype successfully establishes proof-of-concept for leveraging blockchain as an immutable audit trail for AI-driven cybersecurity systems. Future research directions include integrating advanced scaling techniques, such as layer 2 solutions, and extending the blockchain logging capabilities to cover the entire AI model lifecycle, including detailed training logs and comprehensive version histories. This work provides foundational contributions towards building trusted, auditable, and transparent AI solutions in regulated cyber security domains.