SeCTIS: A Framework to Secure CTI Sharing
Dincy R. Arikkat, Mert Cihangiroglu, Mauro Conti, Rafidha Rehiman K., A., Serena Nicolazzo, Antonino Nocera, Vinod P

TL;DR
SeCTIS is a novel framework that combines Swarm Learning and Blockchain to enable privacy-preserving, trustworthy sharing of Cyber Threat Intelligence among organizations, enhancing security and collaboration.
Contribution
It introduces a secure CTI sharing framework using blockchain, Swarm Learning, and Zero Knowledge Proofs to ensure privacy, data quality, and participant trustworthiness.
Findings
Framework demonstrates correctness and performance in experiments
Provides mechanisms for data and model quality assessment
Shows robustness against various attack models
Abstract
The rise of IT-dependent operations in modern organizations has heightened their vulnerability to cyberattacks. As a growing number of organizations include smart, interconnected devices in their systems to automate their processes, the attack surface becomes much bigger, and the complexity and frequency of attacks pose a significant threat. 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 leaks of both proprietary and confidential data. To tackle this problem, we designed a novel framework called SeCTIS (Secure Cyber Threat Intelligence Sharing), integrating Swarm…
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Taxonomy
TopicsResearch Data Management Practices · Web and Library Services · Digital Rights Management and Security
