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The Sound of Malware: A Memory Forensics Approach for Android Malware Analysis via Audio Signals

This paper proposes \approachname, a memory forensics framework that converts Android malware's static bytecode and early-execution memory snapshots into audio waveforms to detect stealthy threats via signal-based structural analysis, achieving up to 98.0% accuracy without relying on traditional feature engineering or disassembly.

Original authors: Silvia Lucia Sanna, Massimo Palozzi, Leonardo Regano, Riccardo Lazzeretti, Giorgio Giacinto

Published 2026-06-08

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CC BY 4.0

Original authors: Silvia Lucia Sanna, Massimo Palozzi, Leonardo Regano, Riccardo Lazzeretti, Giorgio Giacinto

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine you are trying to tell if a person is a spy or a regular citizen.

The Old Way (Traditional Analysis):
Usually, security experts try to catch bad apps by reading their "ID cards" (permissions) or listening to their "spoken words" (API calls). But modern spies are smart. They wear disguises (code obfuscation), hide their true voices (packing), or only speak when no one is looking (dynamic loading). This makes the old methods less effective because the spy changes their appearance just enough to look innocent.

The New Way (RAMwavDroid):
This paper introduces a new detective called RAMwavDroid. Instead of trying to understand the meaning of the app's code, it treats the app like a piece of music.

Here is how it works, step-by-step:

1. Turning Code into Sound

Imagine you have a secret diary written in a strange code. Instead of trying to translate the words, you take every single letter and turn it into a musical note.

If the letter is "A," you play a low note.
If the letter is "Z," you play a high note.

RAMwavDroid does exactly this with computer code. It takes the raw numbers (bytes) that make up an app and turns them directly into an audio file (a WAV or MP3). It doesn't care what the code means; it only cares about the rhythm and pattern of the numbers.

2. The "Snapshot" vs. The "Blueprint"

The researchers tested two different ways to make this sound:

The Blueprint (Static Analysis): They took the app's installation file (the APK) before it was even opened. This is like listening to a recording of a song before the band starts playing. It's a good start, but the song might sound different once the instruments are actually playing.
The Live Performance (Dynamic Analysis): This is the paper's big breakthrough. They waited until the app was actually running on a phone. They took a "snapshot" of the app's memory (RAM) while it was working.
- Analogy: Think of a magician. The "Blueprint" is just the empty stage. The "Live Performance" is the moment the magician pulls a rabbit out of a hat. The rabbit (the malicious code) wasn't visible in the empty stage, but it's definitely there in the memory snapshot.

3. Listening for the "Bad Beat"

Once they turned the code into sound, they used computers trained to recognize music patterns (like how you can tell the difference between a jazz song and a rock song just by listening).

The Good Apps: When converted to sound, they played like a smooth, uniform song.
The Bad Apps (Malware): Because malware often has hidden, repetitive, or chaotic structures (like encrypted payloads or strange loading patterns), they sounded like a song with weird, jarring beats or strange, repeating loops.

4. The Results

The researchers tested this on thousands of apps.

The Blueprint approach was good, getting about 94% accuracy.
The Live Performance approach (using the memory snapshot) was amazing, hitting 98% accuracy.

Why was the Live Performance better?
Because the "Live Performance" captured the app after it had woken up. Even if a spy app hides its identity in the blueprint, it has to reveal its true structure to the computer's memory to do its job. RAMwavDroid catches that reveal.

5. Does the Sound Quality Matter?

The researchers tried different audio formats:

High-Fidelity (WAV): Perfect quality.
Compressed (MP3): Lower quality, like streaming music on a bad connection.

Surprisingly, the MP3 (compressed) version worked almost as well as the high-quality version. This tells us that the "badness" of the malware isn't in the tiny, perfect details of the sound; it's in the big, structural patterns (the rhythm and the beat) that survive even when the sound is compressed.

Summary

RAMwavDroid is like a detective who stops trying to read the suspect's mind and instead listens to the rhythm of their footsteps. Even if the suspect tries to walk quietly or wear different shoes, the unique rhythm of their malicious activity leaves a distinct "sound" in the computer's memory that is very hard to hide.

Key Takeaway: By turning computer code into audio and listening to the "live performance" of an app's memory, this method can spot sneaky malware that traditional methods miss, achieving a 98% success rate.

Technical Summary: RAMwavDroid – A Memory Forensics Approach for Android Malware Analysis via Audio Signals

Problem Statement

Android malware detection faces escalating challenges due to advanced evasion strategies employed by modern threats, including code obfuscation, dynamic loading, packing, and steganographic manipulation. These techniques significantly degrade the effectiveness of traditional signature-based systems and Machine Learning (ML) models that rely on explicit semantic indicators such as permissions, API calls, or control-flow structures. While static analysis is computationally efficient, it is easily evaded; conversely, dynamic analysis offers better resilience but suffers from execution overhead, incomplete behavioral coverage, and susceptibility to anti-analysis techniques. Furthermore, existing audio-based malware detection methods primarily focus on static binaries, failing to capture volatile runtime artifacts such as decrypted payloads, dynamically loaded libraries, and runtime-generated code that are invisible in packaged application binaries.

Methodology: RAMwavDroid

The authors propose RAMwavDroid, a unified framework that bridges memory forensics and audio encoding to detect Android malware. The core concept involves a "modality shift" from semantic program modeling to signal-based structural representation.

1. Data Acquisition and Sonification

The framework processes two types of inputs:

Static Analysis: Extracts the Dalvik bytecode (classes.dex) from APK archives.
Dynamic Analysis: Acquires process-level memory snapshots immediately after application initialization using Fridump (a user-space tool built on Frida). This captures readable memory regions including the heap, stack, shared libraries, and runtime structures without requiring root privileges or kernel modifications.

Binary-to-Audio Mapping:
Both static bytecode and dynamic memory dumps are transformed into audio waveforms through direct binary-to-waveform mapping (Algorithm 1).

Each byte $b_i \in [0, 255]$ is interpreted directly as an audio sample amplitude.
No disassembly, normalization, semantic filtering, or opcode-level abstraction is performed prior to conversion.
This preserves low-level structural patterns (repetition, alignment, padding, encryption artifacts) and treats the binary stream as an ordered numerical signal.

The system experiments with three encoding formats:

WAV8: Lossless, 8-bit PCM (direct byte-to-sample mapping).
WAV16: Lossless, 16-bit PCM (expands dynamic range).
MP3: Lossy, perceptual compression (acts as structured perturbation to test robustness).

2. Feature Extraction and Classification

The resulting audio signals are processed using three distinct paradigms:

Handcrafted Descriptors: Traditional signal processing features (e.g., MFCCs, spectral centroid, zero-crossing rate, RMS energy) extracted via librosa.
Deep Learning (CNN): A 1D Convolutional Neural Network (CNN1D) that learns hierarchical patterns directly from time-frequency representations.
Transformer Embeddings: A pre-trained Wav2Vec2 model (self-supervised on speech data) used to extract high-level contextual embeddings from raw waveforms, leveraging transfer learning.

3. Experimental Setup

Datasets:
- CICMalDroid2020: Used for static and dynamic evaluation (300 benign, 300 malicious).
- VirusTotal/Androzoo: A stealthy malware dataset (500 malicious, 500 benign) containing samples with obfuscation and anti-analysis techniques (2020–2024).
Memory Region Strategies: The dynamic analysis evaluates various combinations of memory regions (e.g., apk_full_stack, base_apk, complete_memory_regions) to identify discriminative runtime artifacts.

Key Contributions

Dynamic Memory Sonification Pipeline: The introduction of a pipeline that integrates process-level memory acquisition with binary-to-audio encoding, enabling the analysis of runtime artifacts alongside static bytecode.
Systematic Evaluation of Encodings and Regions: A comprehensive comparison of audio encoding formats (WAV8, WAV16, MP3) and memory-region selection strategies to identify which runtime artifacts provide the most discriminative power.
Performance Validation: Demonstration that dynamic sonification achieves high accuracy (up to 98.0%), outperforming static sonification and competitive state-of-the-art approaches, without relying on fragile semantic feature engineering.

Results

Static Analysis (CICMalDroid2020)

The best static configuration (WAV8 + Manual Features + Random Forest) achieved 93.9% accuracy.
WAV16 and MP3 encodings showed marginal performance differences (92.3% and 92.6%, respectively), suggesting that higher bit-depth does not add discriminative information and that moderate lossy compression preserves structural characteristics.
Wav2Vec2 embeddings reduced feature extraction time significantly (from 6–7 hours to 2 hours) but resulted in a slight accuracy drop (91.5%).

Dynamic Analysis (CICMalDroid2020)

Peak Performance: 98.0% accuracy with 0 False Positives and 1 False Negative.
Optimal Strategy: This performance was achieved by seven region-selection strategies that combined application code segments (e.g., base.apk, .odex) with execution context components (e.g., stack frames).
Encoding Robustness: While WAV8 had the highest average accuracy (95.8%), all three encodings reached the 98.0% peak under optimal region selection. This indicates that discriminative information relies on coarse-grained structural patterns rather than fine-grained amplitude precision.
Region Importance: Strategies using only stack memory (90.0%) or all memory regions indiscriminately (93.0%) performed worse, highlighting that the interaction between static code and runtime context is critical.

Stealthy Malware Evaluation (VirusTotal Dataset)

On a dataset of 500 obfuscated/anti-analysis malware samples, the best configuration (apk_full_stack with WAV8) achieved approximately 99.0% macro-F1 (98.9% accuracy) using an MLP classifier.
Performance was largely model-independent; Random Forest, boosting methods, CNN1D, and SVM converged to nearly identical scores when informative memory regions were selected.
Failure Analysis: Misclassified samples included a "gray area" app (legitimate VPN with intrusive components) and a structurally simple spyware app lacking complex entropy patterns, suggesting that extreme simplicity or hybrid legitimacy can obscure structural signatures.

Comparison with State-of-the-Art

RAMwavDroid's dynamic sonification (98.0%) outperformed:

Image-based approaches (97.25%).
BlockDroid CNN models (97.38%).
Static sonification configurations.

Significance and Claims

The paper claims that RAMwavDroid demonstrates that structural patterns embedded in runtime memory provide a robust signal representation for Android malware detection. Key findings include:

Modality Shift: Treating malware as a signal rather than a semantic object allows for the detection of patterns (e.g., spectral signatures in spectrograms) that evade traditional static analysis.
Runtime Artifacts are Critical: Dynamic memory snapshots capture decrypted payloads and execution contexts that are invisible in static APKs, significantly improving separability between benign and malicious applications.
Robustness to Compression: The fact that lossy MP3 compression does not degrade peak performance suggests that malware signatures are encoded in stable, coarse-grained structural patterns rather than fragile, fine-grained byte variations.
Model Independence: Once the correct memory regions are selected, the specific classification algorithm (traditional ML, CNN, or Transformer) becomes less critical, implying the signal representation itself is highly discriminative.

The authors conclude that dynamic memory sonification is a promising complementary approach to traditional techniques, offering high accuracy without the need for deobfuscation or complex feature engineering. Future work is proposed to scale to larger datasets and investigate robustness against advanced obfuscation.