This patent describes a system and method for generating cyber-threat scores by leveraging machine learning (ML) and explicitly considering the quality of the sources providing the threat intelligence. The core innovation lies in training an ML model not only on the presence or absence of threat indicators and their classifications but also on "quality metrics" associated with the sources. During inference, the system identifies votes from various sources on a new indicator, assesses the quality of those sources, and generates a threat score based on the trained ML model, which has learned to weight source reliability. This approach aims to improve the accuracy and reliability of threat intelligence by mitigating the impact of low-quality or unreliable sources.

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