SeCTIS: A framework to Secure CTI Sharing

• Published in: Future generation computer systems 2025-03, Vol.164, p.107562, Article 107562
• Main Authors: Arikkat, Dincy R., Cihangiroglu, Mert, Conti, Mauro, K.A., Rafidha Rehiman, Nicolazzo, Serena, Nocera, Antonino, P., Vinod
• Format: Article
• Language: English
• Subjects: Blockchain; CTI sharing; Cyber threat intelligence; Federated learning; Internet of Things (IoT); Swarm learning; Zero knowledge proof
• ISSN: 0167-739X
• DOI: 10.1016/j.future.2024.107562

The rise of IT-dependent operations in modern organizations has heightened their vulnerability to cyberattacks. Organizations are inadvertently enlarging their vulnerability to cyber threats by integrating more interconnected devices into their operations, which makes these threats both more sophisticated and more common. Consequently, organizations have been compelled to seek innovative approaches to mitigate the menaces inherent in their infrastructure. In response, considerable research efforts have been directed towards creating effective solutions for sharing Cyber Threat Intelligence (CTI). Current information-sharing methods lack privacy safeguards, leaving organizations vulnerable to proprietary and confidential data leaks. To tackle this problem, we designed a novel framework called SeCTIS (Secure Cyber Threat Intelligence Sharing), integrating Swarm Learning and Blockchain technologies to enable businesses to collaborate, preserving the privacy of their CTI data. Moreover, our approach provides a way to assess the data and model quality and the trustworthiness of all the participants leveraging some validators through Zero Knowledge Proofs. Extensive experimentation has confirmed the accuracy and performance of our framework. Furthermore, our detailed attack model analyzes its resistance to attacks that could impact data and model quality. •Definition of a Swarm Learning approach for collaborative CTI.•Definition of a Blockchain-based solution for privacy preservation in CTI sharing.•Secure CTI validation using a consensus mechanism and Zero-Knowledge Proof.