中圖分類號(hào)：TP309.5 文獻(xiàn)標(biāo)志碼：A DOI: 10.16157/j.issn.0258-7998.245881 中文引用格式： 葛繼科，何明坤,，陳祖琴,，等. 基于多模態(tài)特征融合的Android惡意程序檢測(cè)方法研究[J]. 電子技術(shù)應(yīng)用，2025,，51(1)：62-68. 英文引用格式： Ge Jike,，He Mingkun,，Chen Zuqin，et al. Research on Android malware detection method based on multimodal feature fusion[J]. Application of Electronic Technique,，2025,，51(1)：62-68.
Research on Android malware detection method based on multimodal feature fusion
Ge Jike，He Mingkun,，Chen Zuqin,，Ling Jin，Zhang Yifan
School of Computer Science and Engineering,， Chongqing University of Science and Technology
Abstract： Existing Android malware detection methods mainly use single-modal data to characterize program features, but fail to fully mine and fuse different feature information, resulting in unsatisfactory detection results. In order to improve the accuracy and robustness of detection, a method for detecting Android malware based on multimodal feature fusion is proposed. Firstly, the permission information is encoded and the Dalvik bytecode data is visualized as a “vector” RGB image. Then, a feedforward neural network and a convolutional neural network are constructed to extract features from the data represented by text and image modalities, respectively. Finally, different weights are assigned to the extracted feature vectors of different modalities, which are added and fused before classification. Experimental results show that the recognition accuracy and F1 score of this method for Android malware both reach 98.66%, and it has good robustness.
Key words : Android,；malware；multimodality,；feedforward neural network,；convolutional neural network
