Research on Android Malware Detection Method based on the Improved Graph Attention Mechanism Model
In the context of the spread of malware,the demand for malware detection is increasing.This paper presents an Android malware detection method based on an improved graph attention mechanism model.The methodology in-volves extracting the API call graph through static analysis,which shows the behavior of the application.Subsequently,the structural features and content features are acquired from the API call graph by using the SDNE graph embedding al-gorithm.In the process of model learning,a strategy is adopted to calculate the bidirectional graph attention weights,ai-ming to improve the retention of similar nodes and enhance the similarity between node properties.Finally,a weight-a-daptive representation is generated with the help of the self-attention convolution layer,and a graph embedding represen-tation is generated in the pooling layer for use in the detection task.The experimental results are based on the CICMal-Droid 2020 data set,showing that this method shows high effectiveness in the field of Android malware detection,with an accuracy of 97.90%.Compared with the original graph attention network model,it improves the accuracy by 0.03%,verifying the practicability and effectiveness of the proposed method.The results show the potential to deal with growing malware threats and to provide a more accurate and reliable solution for Android malware detection.
API call graphSDNE embeddingbidirectional graph attentionAndroid malware detection
万方数据
维普
