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Dual Virtual Hubs enable ICFlowNet to achieve a remarkable 9.13-point improvement in long-range F1 scores, revolutionizing how we predict indirect control-flow in stripped binaries.
MAOL outperforms existing methods in fine-grained defect grading by effectively integrating morphological cues and adaptive thresholds, achieving robust performance even with noisy data.
