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<p>The increasing complexity of Android malware has increased the need for efficient detection methods. Researchers have introduced new frameworks for analyzing Android malware in response to the growing threat of malicious applications. Traditional static analysis methods, which are widely used, are susceptible to obfuscation and can be bypassed easily. However, although dynamic analysis is more resilient, it is computationally intensive and costly to implement. In this paper, we introduce MalWave, a novel approach that uses audio signal processing to detect Android malware by converting Dalvik Executable (DEX) file sequences into audio signals. The extracted audio fingerprints are used as features for classification, addressing (i) malware detection, (ii) family classification, and (iii) packed malware detection. Evaluated on the AMD and AndroZoo datasets, MalWave achieves an F1+ score of 82.6% for malware detection and 68.7% for family classification, particularly in mostly represented categories. Despite challenges in detecting packed malware, MalWave demonstrates high computational efficiency, with feature extraction taking just 0.3 seconds on average per sample, making it a suitable tool for real-time detection in resource-constrained environments.</p>

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The sound of malware: an audio fingerprinting malware detection method

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月11日

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  • Efstratios Vasilellis,
  • Thanos Katsiolis,
  • Dimitris Gritzalis,
  • George Stergiopoulos,
  • Christina Sotiriou

摘要

The increasing complexity of Android malware has increased the need for efficient detection methods. Researchers have introduced new frameworks for analyzing Android malware in response to the growing threat of malicious applications. Traditional static analysis methods, which are widely used, are susceptible to obfuscation and can be bypassed easily. However, although dynamic analysis is more resilient, it is computationally intensive and costly to implement. In this paper, we introduce MalWave, a novel approach that uses audio signal processing to detect Android malware by converting Dalvik Executable (DEX) file sequences into audio signals. The extracted audio fingerprints are used as features for classification, addressing (i) malware detection, (ii) family classification, and (iii) packed malware detection. Evaluated on the AMD and AndroZoo datasets, MalWave achieves an F1+ score of 82.6% for malware detection and 68.7% for family classification, particularly in mostly represented categories. Despite challenges in detecting packed malware, MalWave demonstrates high computational efficiency, with feature extraction taking just 0.3 seconds on average per sample, making it a suitable tool for real-time detection in resource-constrained environments.