Android Malware Detection Based on Hypergraph Neural Networks Article Swipe
Dehua Zhang
,
Xiangbo Wu
,
Erlu He
,
Xiaobo Guo
,
Xiaopeng Yang
,
Ruibo Li
,
Hao Li
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/app132312629
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/app132312629
Android has been the most widely used operating system for mobile phones over the past few years. Malicious attacks against android are a major privacy and security concern. Malware detection techniques for android applications are therefore significant. A class of methods using Function Call Graphs (FCGs) for android malware detection has shown great potential. The relationships between functions are limited to simple binary relationships (i.e., graphs) in these methods. However, one function often calls several other functions to produce specific effects in android applications, which cannot be captured with FCGs. In this paper, we propose to formalize android malware detection as a hypergraph-level classification task. A hypergraph is a topology capable of portraying complex relationships between multiple vertices, which can better characterize the functional behavior of android applications. We model android applications using hypergraphs and extract the embedded features of android applications using hypergraph neural networks to represent the functional behavior of android applications. Hypergraph neural networks can encode high-order data correlation in a hypergraph structure for data representation learning. In experiments, we validate the gaining effect of hypergraphs on detection performance across two open-source android application datasets. Especially, HGNNP obtains the best classification performance of 91.10% on the Malnet-Tiny dataset and 97.1% on the Drebin dataset, which outperforms all baseline methods.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/app132312629
- https://www.mdpi.com/2076-3417/13/23/12629/pdf?version=1700751804
- OA Status
- gold
- Cited By
- 5
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4388938950
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https://openalex.org/W4388938950Canonical identifier for this work in OpenAlex
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https://doi.org/10.3390/app132312629Digital Object Identifier
- Title
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Android Malware Detection Based on Hypergraph Neural NetworksWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-11-23Full publication date if available
- Authors
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Dehua Zhang, Xiangbo Wu, Erlu He, Xiaobo Guo, Xiaopeng Yang, Ruibo Li, Hao LiList of authors in order
- Landing page
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https://doi.org/10.3390/app132312629Publisher landing page
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https://www.mdpi.com/2076-3417/13/23/12629/pdf?version=1700751804Direct link to full text PDF
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2076-3417/13/23/12629/pdf?version=1700751804Direct OA link when available
- Concepts
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Android (operating system), Hypergraph, Computer science, Android malware, Malware, Theoretical computer science, Artificial intelligence, Machine learning, Data mining, Computer security, Operating system, Mathematics, Discrete mathematicsTop concepts (fields/topics) attached by OpenAlex
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5Total citation count in OpenAlex
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2025: 4, 2024: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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