Explore a 21-minute conference talk that delves into LEMNA, a novel approach for explaining deep learning-based security applications. Learn about the challenges of opaque deep learning models in security-critical domains and the limitations of existing explanation techniques. Discover how LEMNA addresses these issues by supporting locally non-linear decision boundaries and modeling feature dependency. Gain insights into deriving explanations from deep neural networks, evaluating explanation accuracy, and practical applications such as identifying binary function starts. Understand how LEMNA contributes to building trust in target models and aids in troubleshooting and patching model errors, ultimately enhancing the transparency and reliability of deep learning in security contexts.