AI-Blockchain Integration for Real-Time Cybersecurity: System Design and Evaluation

This paper proposes and evaluates a novel real-time cybersecurity framework integrating artificial intelligence (AI) and blockchain technology to enhance the detection and auditability of cyber threats. Traditional cybersecurity approaches often lack transparency and robustness in logging and verifying AI-generated decisions, hindering forensic investigations and regulatory compliance. To address these challenges, we developed an integrated solution combining a convolutional neural network (CNN)-based anomaly detection module with a permissioned Ethereum blockchain to securely log and immutably store AI-generated alerts and relevant metadata. The proposed system employs smart contracts to automatically validate AI alerts and ensure data integrity and transparency, significantly enhancing auditability and forensic analysis capabilities. To rigorously test and validate our solution, we conducted comprehensive experiments using the CICIDS2017 dataset and evaluated the system’s detection accuracy, precision, recall, and real-time responsiveness. Additionally, we performed penetration testing and security assessments to verify system resilience against common cybersecurity threats. Results demonstrate that our AI-blockchain integrated solution achieves superior detection performance while ensuring real-time logging, transparency, and auditability. The integration significantly strengthens system robustness, reduces false positives, and provides clear benefits for cybersecurity management, especially in regulated environments. This paper concludes by outlining potential avenues for future research, particularly extending blockchain scalability, privacy enhancements, and optimizing performance for high-throughput cybersecurity applications.