BERTDetect: A Neural Topic Modelling Approach for Android Malware Detection

• URL: http://arxiv.org/abs/2503.18043v1
• Date: Sun, 23 Mar 2025 12:09:44 GMT
• Title: BERTDetect: A Neural Topic Modelling Approach for Android Malware Detection
• Authors: Nishavi Ranaweera, Jiarui Xu, Suranga Seneviratne, Aruna Seneviratne,
• Abstract summary: Web access today occurs predominantly through mobile devices, with Android representing a significant share of the mobile device market.<n>Despite efforts to combat malicious attacks through tools like Google Play Protect and antivirus software, new and evolved malware continues to infiltrate Android devices.<n>Source code analysis is effective but limited, as attackers quickly abandon old malware for new variants to evade detection.
• Score: 13.387599470973807
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Web access today occurs predominantly through mobile devices, with Android representing a significant share of the mobile device market. This widespread usage makes Android a prime target for malicious attacks. Despite efforts to combat malicious attacks through tools like Google Play Protect and antivirus software, new and evolved malware continues to infiltrate Android devices. Source code analysis is effective but limited, as attackers quickly abandon old malware for new variants to evade detection. Therefore, there is a need for alternative methods that complement source code analysis. Prior research investigated clustering applications based on their descriptions and identified outliers in these clusters by API usage as malware. However, these works often used traditional techniques such as Latent Dirichlet Allocation (LDA) and k-means clustering, that do not capture the nuanced semantic structures present in app descriptions. To this end, in this paper, we propose BERTDetect, which leverages the BERTopic neural topic modelling to effectively capture the latent topics in app descriptions. The resulting topic clusters are comparatively more coherent than previous methods and represent the app functionalities well. Our results demonstrate that BERTDetect outperforms other baselines, achieving ~10% relative improvement in F1 score.

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