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Machine learning-based Android malware classifiers achieve high accuracy in stationary environments but struggle with concept drift.
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TESSERACT: Eliminating experimental bias in malware classification across space and time. In 28th USENIX Security Symposium (USENIX Security 19) . 729–746
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Drift Forensics of Malware Classifiers. In Proc. of the ACM Workshop on Artificial Intelligence and Security (AISec) . ACM
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FINER: Enhancing State-of-the-art Classifiers with Feature Attribution to Facilitate Security Analysis. In Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security . 416–430
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Android Based Malware Detection Technique Using Machine Learning Algorithms. In 2024 First International Conference on Pioneering Developments in Computer Science & Digital Technologies (IC2SDT) . IEEE, 1–6
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Detecting Android Malware by Visualizing App Behaviors from Multiple Complementary Views
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A novel Android malware detection method with API semantics extraction
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MsDroid: Identifying Malicious Snippets for Android Malware Detection
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