SeCTIS: A Framework to Secure CTI Sharing

• URL: http://arxiv.org/abs/2406.14102v1
• Date: Thu, 20 Jun 2024 08:34:50 GMT
• Title: SeCTIS: A Framework to Secure CTI Sharing
• Authors: Dincy R. Arikkat, Mert Cihangiroglu, Mauro Conti, Rafidha Rehiman K. A., Serena Nicolazzo, Antonino Nocera, Vinod P,
• Abstract summary: The rise of IT-dependent operations in modern organizations has heightened their vulnerability to cyberattacks. Current information-sharing methods lack privacy safeguards, leaving organizations vulnerable to leaks of both proprietary and confidential data. We design a novel framework called SeCTIS (Secure Cyber Threat Intelligence Sharing) to enable businesses to collaborate, preserving the privacy of their CTI data.
• Score: 13.251593345960265
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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. An extensive experimental campaign demonstrates our framework's correctness and performance, and the detailed attack model discusses its robustness against attacks in the context of data and model quality.

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