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Digital Forensics and Cyber Crime. 14th EAI International Conference, ICDF2C 2023, New York City, NY, USA, November 30, 2023, Proceedings, Part II

Research Article

An Android Malware Detection Method Based on Optimized Feature Extraction Using Graph Convolutional Network

Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-56583-0_19,
        author={Zhiqiang Wang and Zhuoyue Wang and Ying Zhang},
        title={An Android Malware Detection Method Based on Optimized Feature Extraction Using Graph Convolutional Network},
        proceedings={Digital Forensics and Cyber Crime. 14th EAI International Conference, ICDF2C 2023, New York City, NY, USA, November 30, 2023, Proceedings, Part II},
        proceedings_a={ICDF2C PART 2},
        year={2024},
        month={4},
        keywords={Android Malware Graph Convolutional Networks Static Analysis Graph Features},
        doi={10.1007/978-3-031-56583-0_19}
    }
    
  • Zhiqiang Wang
    Zhuoyue Wang
    Ying Zhang
    Year: 2024
    An Android Malware Detection Method Based on Optimized Feature Extraction Using Graph Convolutional Network
    ICDF2C PART 2
    Springer
    DOI: 10.1007/978-3-031-56583-0_19
Zhiqiang Wang1,*, Zhuoyue Wang1, Ying Zhang1
  • 1: Beijing Electronic Science and Technology Institute
*Contact email: wangzq@besti.edu.cn

Abstract

With the development of the mobile Internet, mobile devices have been extensively promoted and popularized. Android, as the current popular mobile intelligent operating system, has encountered problems such as the explosive growth of Android malware while bringing convenience to users. The traditional Android malware detection methods have some problems, such as low detection accuracy and difficulty in detecting unknown malware. This paper proposes an Android malware detection method named Android malware detection method based on graph convolutional neural network (AGCN) based on the graph convolutional network (GCN) to solve the above problems. Firstly, we divide the Android software datasets according to family and software features and construct a directed network topology graph. At the same time, the permission features of APK files are extracted and vectorized. Then, we use GCN to learn the features of Android APK files… Finally, we compare AGCN with a multilayer perceptron (MLP), long and short-term memory (LSTM) neural network, bi-directional long and short-term memory (bi-LSTM) neural network, and deep confidence neural network (DCNN) for experiments. Experimental results show that the model has an accuracy of 98.55% for malware detection, demonstrating the detection method’s effectiveness.

Keywords
Android Malware Graph Convolutional Networks Static Analysis Graph Features
Published
2024-04-03
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-56583-0_19
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