AMMF: Attention-Driven Multi-Feature Fusion for Scalable Cross-Architecture Binary Vulnerability

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This presentation introduces AMMF, an attention-driven multi-feature fusion approach for scalable cross-architecture binary vulnerability detection. The research targets security researchers and reverse engineers working on binary analysis at scale, presenting a novel method that combines multiple feature representations to detect vulnerabilities across different CPU architectures using deep learning techniques.