Transforms static bytecode and memory snapshots of Android apps into audio signals processed by spectral features and deep learning models to detect malware at up to 98% accuracy.
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Pith papers citing it
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cs.CR 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Hybrid feature fusion of API calls and n-grams with voting-based classifier fusion achieves 99.72% accuracy and 0.989 AUC for malware family classification on Microsoft dataset.
citing papers explorer
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The Sound of Malware: A Memory Forensics Approach for Android Malware Analysis via Audio Signals
Transforms static bytecode and memory snapshots of Android apps into audio signals processed by spectral features and deep learning models to detect malware at up to 98% accuracy.
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A Hybrid Approach For Malware Classification Using Secondary Features Fusion
Hybrid feature fusion of API calls and n-grams with voting-based classifier fusion achieves 99.72% accuracy and 0.989 AUC for malware family classification on Microsoft dataset.