Cybersecurity Information Exchange with Privacy (CYBEX-P) and TAHOE -- A Cyberthreat Language

TL;DR

This paper presents CYBEX-P, a privacy-preserving cybersecurity information sharing framework utilizing a novel threat language TAHOE, enabling secure, automated threat data exchange and analysis.

Contribution

It introduces CYBEX-P, a comprehensive framework combining a new cyber threat language TAHOE and a universal query language TDQL for secure, automated threat data sharing and analysis.

Findings

CYBEX-P effectively supports privacy-controlled data sharing.

TAHOE enables structured, correlated threat data analysis.

ThreatRank detects emerging malicious events.

Abstract

Cybersecurity information sharing (CIS) is envisioned to protect organizations more effectively from advanced cyber attacks. However, a completely automated CIS platform is not widely adopted. The major challenges are: (1) the absence of a robust cyber threat language (CTL) and (2) the concerns over data privacy. This work introduces Cybersecurity Information Exchangewith Privacy (CYBEX-P), as a CIS framework, to tackle these challenges. CYBEX-P allows organizations to share heterogeneous data with granular, attribute based privacy control. It correlates the data to automatically generate intuitive reports and defensive rules. To achieve such versatility, we have developed TAHOE - a graph based CTL. TAHOE is a structure for storing,sharing and analyzing threat data. It also intrinsically correlates the data. We have further developed a universal Threat Data Query Language (TDQL). In this paper, we propose the system architecture for CYBEX-P. We then discuss its scalability and privacy features along with a use case of CYBEX-P providing Infrastructure as a Service (IaaS). We further introduce TAHOE& TDQL as better alternatives to existing CTLs and formulate ThreatRank - an algorithm to detect new malicious even