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<p>With the development of mobile internet, the open Android operating system has become the most widely used mobile platform globally, leading to a surge in malware that poses serious threats to user device security. Current Android malware detection methods mainly rely on a single feature set, making it difficult to comprehensively represent the characteristics of Android applications. To address this limitation, this paper proposes an Android malware detection method called GBADroid. GBADroid comprehensively characterizes Android software by considering multi-view features. Specifically, it first matches against a list of dangerous permissions to identify potential risks and then employs an information gain algorithm and a Bidirectional Gated Recurrent Unit (BiGRU) to extract opcode features. It also constructs a function call graph (FCG) to extract graph features using Graph Sample and Aggregate (GraphSAGE) algorithm. Experimental results show that GBADroid achieves a detection accuracy of 98.73%, demonstrating superior performance compared to existing methods.</p>

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GBADroid: an Android malware detection method based on multi-view feature fusion

  • 期刊论文

  • 发表日期：

    2025年2月14日

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  • Yi Meng,
  • Nurbol Luktarhan,
  • Xiaotong Yang,
  • Guodong Zhao

摘要

With the development of mobile internet, the open Android operating system has become the most widely used mobile platform globally, leading to a surge in malware that poses serious threats to user device security. Current Android malware detection methods mainly rely on a single feature set, making it difficult to comprehensively represent the characteristics of Android applications. To address this limitation, this paper proposes an Android malware detection method called GBADroid. GBADroid comprehensively characterizes Android software by considering multi-view features. Specifically, it first matches against a list of dangerous permissions to identify potential risks and then employs an information gain algorithm and a Bidirectional Gated Recurrent Unit (BiGRU) to extract opcode features. It also constructs a function call graph (FCG) to extract graph features using Graph Sample and Aggregate (GraphSAGE) algorithm. Experimental results show that GBADroid achieves a detection accuracy of 98.73%, demonstrating superior performance compared to existing methods.