TrustFed-CTI: A Trust-Aware Federated Learning Framework for Privacy-Preserving Cyber Threat Intelligence Sharing Across Distributed Organizations

The rapid evolution of cyber threats requires intelligence sharing between organizations while ensuring data privacy and contributor credibility. Existing centralized cyber threat intelligence (CTI) systems suffer from single points of failure, privacy concerns, and vulnerability to adversarial manipulation. This paper introduces TrustFed-CTI, a novel trust-aware federated learning framework designed for privacy-preserving CTI collaboration across distributed organizations. The framework integrates a dynamic reputation-based trust scoring system to evaluate member reliability, along with differential privacy and secure multi-party computation to safeguard sensitive information. A trust-weighted model aggregation mechanism further mitigates the impact of adversarial participants. A context-aware trust engine continuously monitors the consistency of threat patterns, authenticity of data sources, and contribution quality to dynamically adjust trust scores. Extensive experiments on practical datasets including APT campaign reports, MITRE ATT&CK indicators, and honeypot logs demonstrate a 22.6% improvement in detection accuracy, 28% faster convergence, and robust resistance to up to 35% malicious participants. The proposed framework effectively addresses critical vulnerabilities in decentralized CTI collaboration, offering a scalable and privacy-preserving mechanism for secure intelligence sharing without compromising organizational autonomy.