Weighted quality criteria for cyber threat intelligence: assessment and prioritisation in the MISP data model

Abstract

Cyber Threat Intelligence (CTI) plays a crucial role in cybersecurity risk management, providing valuable insights to organisations for threat identification and mitigation. The continuous and rapid increase of CTI volume, velocity, and variety requires a systematic approach to assessing their quality, operational significance, and actionable contribution. Threat Intelligence Platforms (TIPs) underpin the sharing and analysis of CTI, enabling organisations to collaborate and enhance their defensive measures ensuring the intelligence shared is effective and reliable for mitigating cyber threats. The assessment of CTI demands a detailed and comprehensive consideration of various factors addressing important quality and operational properties, such as their extensive approach, accuracy, timeliness, and standardisation. Drawing upon the MISP data model, this study defines quantifiable quality metrics based on simple mathematical equations. In addition, this study defines 2 approaches for weighting those criteria based on a subjective (fuzzy-Analytic Hierarchy Process) and an objective (Entropy-based) approach. Current CTI evaluation practices either lack standardized, comprehensive and quantifiable quality metrics or fail to balance subjective expert judgment with objective data variability. Moreover, existing models rarely integrate with operational platforms, limiting their practical applicability. This study addresses these gaps by proposing a framework that formally defines quality metrics, applies dual weighting mechanisms, and supports prioritization directly within a TIP context. By integrating quantifiable quality equations based on the MISP data model with diverse weighting approaches, this proposal enables researchers and practitioners to systematically assess and prioritize trustworthy, high-quality CTI. The dual weighting strategy allows organizations to flexibly adapt evaluations either to subjective expert-driven priority settings, or to objective, data-driven ones, depending on operational needs and available contextual knowledge. This approach facilitates informed decision-making, enhancing the organisation’s cybersecurity posture and resilience against evolving threats. This paper illustrates the potential of the proposed approach as well as demonstrates its benefits by implementing a proof of concept technical implementation on real Open Source Intelligence data, showcasing its utility in underpinning cybersecurity measures and facilitating threat response strategies.