scholarly journals An Android Malware Detection Approach Using Community Structures of Weighted Function Call Graphs

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 17478-17486 ◽  
Author(s):  
Yao Du ◽  
Junfeng Wang ◽  
Qi Li
Keyword(s):  
Malware Detection ◽  
Community Structures ◽  
Weighted Function ◽  
Android Malware ◽  
Android Malware Detection ◽  
Detection Approach ◽  
Function Call ◽  
Call Graphs

Learning features from enhanced function call graphs for Android malware detection

2021 ◽  
Vol 423 ◽  
pp. 301-307
Author(s):  
Minghui Cai ◽  
Yuan Jiang ◽  
Cuiying Gao ◽  
Heng Li ◽  
Wei Yuan
Keyword(s):  
Malware Detection ◽  
Android Malware ◽  
Android Malware Detection ◽  
Function Call ◽  
Call Graphs

scholarly journals Android Malware Detection Based on Structural Features of the Function Call Graph

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 186
Author(s):  
Yang Yang ◽  
Xuehui Du ◽  
Zhi Yang ◽  
Xing Liu
Keyword(s):  
Malware Detection ◽  
Structural Features ◽  
Coarse Grained ◽  
Detection Methods ◽  
Convolutional Network ◽  
Android Malware ◽  
Call Graph ◽  
Android Apps ◽  
Android Malware Detection ◽  
Function Call

The openness of Android operating system not only brings convenience to users, but also leads to the attack threat from a large number of malicious applications (apps). Thus malware detection has become the research focus in the field of mobile security. In order to solve the problem of more coarse-grained feature selection and larger feature loss of graph structure existing in the current detection methods, we put forward a method named DGCNDroid for Android malware detection, which is based on the deep graph convolutional network. Our method starts by generating a function call graph for the decompiled Android application. Then the function call subgraph containing the sensitive application programming interface (API) is extracted. Finally, the function call subgraphs with structural features are trained as the input of the deep graph convolutional network. Thus the detection and classification of malicious apps can be realized. Through experimentation on a dataset containing 11,120 Android apps, the method proposed in this paper can achieve detection accuracy of 98.2%, which is higher than other existing detection methods.

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