<ul><li>Machine Learning (ML) promises to enhance the efficacy of Android Malware Detection (AMD) but is vulnerable to realistic evasion attacks.</li><li>Defenders aim to identify susceptible regions in the feature space where ML models are prone to deception.</li><li>A proposed approach introduces a new interpretation of Android domain constraints in the feature space and employs a novel technique to learn them.</li><li>Empirical evaluations show effective detection of Adversarial Examples (AEs) using learned domain constraints and improved robustness against realizable AEs generated by unknown problem-space transformations.</li></ul>

Level Up with ML Vulnerability Identification: Leveraging Domain Constraints in Feature Space for Robust Android Malware Detection

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