Light up that Droid! On the Effectiveness of Static Analysis Features against App Obfuscation for Android Malware Detection

• URL: http://arxiv.org/abs/2310.15645v1
• Date: Tue, 24 Oct 2023 09:07:23 GMT
• Title: Light up that Droid! On the Effectiveness of Static Analysis Features against App Obfuscation for Android Malware Detection
• Authors: Borja Molina-Coronado, Antonio Ruggia, Usue Mori, Alessio Merlo, Alexander Mendiburu, Jose Miguel-Alonso
• Abstract summary: Malware authors have seen obfuscation as the mean to bypass malware detectors based on static analysis features. In this article we assess the impact of specific obfuscation techniques on common features extracted using static analysis. We propose a ML malware detector for Android that is robust against obfuscation and outperforms current state-of-the-art detectors.
• Score: 42.50353398405467
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Malware authors have seen obfuscation as the mean to bypass malware detectors based on static analysis features. For Android, several studies have confirmed that many anti-malware products are easily evaded with simple program transformations. As opposed to these works, ML detection proposals for Android leveraging static analysis features have also been proposed as obfuscation-resilient. Therefore, it needs to be determined to what extent the use of a specific obfuscation strategy or tool poses a risk for the validity of ML malware detectors for Android based on static analysis features. To shed some light in this regard, in this article we assess the impact of specific obfuscation techniques on common features extracted using static analysis and determine whether the changes are significant enough to undermine the effectiveness of ML malware detectors that rely on these features. The experimental results suggest that obfuscation techniques affect all static analysis features to varying degrees across different tools. However, certain features retain their validity for ML malware detection even in the presence of obfuscation. Based on these findings, we propose a ML malware detector for Android that is robust against obfuscation and outperforms current state-of-the-art detectors.

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