AC_MAPPER : a robust approach to ATT&CK technique classification using input augmentation and class rebalancing
The detection and classification of adversarial techniques from cyber threat intelligence (CTI) text is a critical task in threat analysis and mitigation. While recent transformer-based models have shown promise, their general-purpose nature often limits effectiveness on complex, domain-specific datasets. In this paper, we present a novel model designed to address the challenges of technique class
