A Secure and Explainable Cyber Threat Intelligence Framework for Distributed Digital Infrastructures

The rapid adoption of IoT, edge computing and cloud platforms has introduced extensive cybersecurity challenges associated with data privacy, trust management and secure exchange of threat intelligence. The traditional security approaches typically depend on centralized processing, where large volumes of sensitive information are transferred and stored. This results into increase in the risk of privacy breaches. This paper presents the implementation of a privacy preserving cybersecurity framework that combines federated learning, differential privacy, blockchain technology and explainable artificial intelligence within a unified architecture. The proposed framework supports collaborative intrusion detection by allowing participating entities to train shared models locally without exposing raw data. It ensures transparency and trust throughout the learning process. A prototype was implemented using distributed learning nodes, a permissioned blockchain network and explainable analytics modules. The implementation integrates decentralized model training, privacy aware parameter sharing, blockchain based verification and interpretable threat classification to enable secure cybersecurity operations across heterogeneous computing environments. In turn, the methodology determines that these complementary technologies can be effectively integrated to deliver scalable, transparent and trustworthy cyber defense for next generation digital ecosystems.