@Article{cmes.2026.077799,
AUTHOR = {Chun-I Fan, Sheng-Feng Lu, Cheng-Han Shie, Ming-Feng Tsai, Tomohiro Morikawa, Takeshi Takahashi, Tao Ban},
TITLE = {A Graph-Based Interpretable Framework for Effective Android Malware Detection<sup>#</sup>},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {147},
YEAR = {2026},
NUMBER = {1},
PAGES = {0--0},
URL = {http://www.techscience.com/CMES/v147n1/67127},
ISSN = {1526-1506},
ABSTRACT = {Due to its partly open-source architecture, which allows for application analysis and repackaging, along with its large market share, the Android operating system is a main target for malware. In recent years, researchers have widely adopted neural network-based methods for detecting Android malware, achieving impressive results but without interpretability. Interpretability is crucial for showing how models behave and identifying biases in their predictions, which helps in validating and improving them. Additionally, in urgent malware analysis situations, interpretability lets analysts quickly assess harmful behaviors and aids in future malware development and investigation. Therefore, interpretability is vital for ensuring that neural network-based malware detection models are trustworthy, predictable, and strong. To address these issues, we propose an interpretable Graph Attention Network (GAT)-based framework for Android malware detection. This framework includes data flow analysis of Android applications to identify malicious behaviors, providing clarity through the attention mechanism of GAT. Analysts and researchers can access detailed information, such as the names and execution order of the involved Android APIs, allowing for better validation and security checks. Experimental results show that our framework achieves a precision of 97.4%. Additionally, case studies highlight the insights that researchers can gain by using this framework.},
DOI = {10.32604/cmes.2026.077799}
}