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.
Machine Learning with Applications16, 100546 (Jun 2024)
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.CR 2years
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UNVERDICTED 2representative citing papers
SecRL-Prune learns layer-wise pruning policies via RL on CodeLLMs, preserving higher pass@k and var@k than baselines at 10-30% compression on HumanEval and enabling semantics-preserving mutations that reduce malware detections in a case study.
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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SecRL-Prune: Structured Reinforcement Learning-Based Pruning of CodeLLMs for Preserving Adversarial Code Mutation
SecRL-Prune learns layer-wise pruning policies via RL on CodeLLMs, preserving higher pass@k and var@k than baselines at 10-30% compression on HumanEval and enabling semantics-preserving mutations that reduce malware detections in a case study.