Federated and Collaborative Cyber Threat Intelligence (CTI) Sharing Frameworks

Abstract

Cyber threats have grown in complexity, scale, and coordination over the last decade. Traditional centralized Cyber Threat Intelligence (CTI) sharing models face challenges related to privacy, trust, scalability, and legal compliance. As organizations become increasingly interconnected, there is a strong need for collaborative yet privacy-preserving intelligence exchange mechanisms. Federated and collaborative CTI sharing frameworks have emerged as promising approaches that enable distributed entities to share threat insights without exposing sensitive internal data. These frameworks combine federated learning, secure multiparty computation, blockchain-based trust models, and standardized threat formats such as STIX and TAXII. This paper provides a comprehensive review of federated and collaborative CTI sharing architectures, key enabling technologies, implementation models, benefits, and limitations. It also discusses trust establishment mechanisms, privacy preservation strategies, and real-world adoption challenges. A comparative analysis of centralized and federated CTI models is presented along with a conceptual architectural framework. The study concludes by identifying open research directions such as incentive mechanisms, adversarial robustness, and AI-driven automated intelligence validation.

KEYWORDS: Cyber Threat Intelligence (CTI), Federated Learning, Collaborative Security, Threat Sharing, Blockchain Security, Privacy Preservation, Secure Multiparty Computation, STIX/TAXII