Intelligent and Auditable: A Hybrid AI and Distributed Ledger Framework for Modern Cybersecurity

Abstract

Modern cyber threats, known for their complexity and constant change, surpass traditional intrusion detection systems (IDS). This paper explores a new security approach that combines Artificial Intelligence (AI) with decentralized architectures to develop IDS that are robust, scalable, and protect user privacy. It examines the core roles of Federated Learning (FL) and Blockchain, highlighting three main research challenges: The vulnerability of AI models to adversarial attacks, privacy and data integrity concerns in collaborative learning, and performance limitations in distributed systems. To address these issues, we suggest solutions such as adversarial training, differential privacy, and lightweight consensus mechanisms. Our analysis of case studies shows that hybrid FL-Blockchain systems outperform traditional methods in practical application environments.