TRACE achieves 97% macro F1 on temporal out-of-distribution prediction of organizational exploit targets using contrastive learning on a 129k-sample multi-source dataset, outperforming 17 baselines.
A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection,
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Vendor-Conditioned Contrastive Learning for Predicting Organizational Cyber Threat Targets
TRACE achieves 97% macro F1 on temporal out-of-distribution prediction of organizational exploit targets using contrastive learning on a 129k-sample multi-source dataset, outperforming 17 baselines.