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- W3208596824 abstract "Large-scale datasets for mobile applications (a.k.a.“app”) such as AndroZoo++ and AndroVault have become powerful assets for malware detection and channel monitoring. However, these datasets focus on the scale of apps while most apps in them remain isolated and cannot easily be referenced and linked from other apps. To fill these gaps, in this paper, we present a mobile application knowledge graph, namely MAKG, which aims to collect the apps from various resources. We design a lightweight ontology of apps. It can bring a well-defined schema of collected apps so that these apps could share more linkage with each other. Moreover, we evaluate the algorithms of information extraction and knowledge alignment during the process of construction, and select the competent models to enrich the structured triples in MAKG. Finally, we list three use-cases about MAKG that are helpful to provide better services for security analysts and users." @default.
- W3208596824 created "2021-11-08" @default.
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- W3208596824 date "2021-01-01" @default.
- W3208596824 modified "2023-10-07" @default.
- W3208596824 title "MAKG: A Mobile Application Knowledge Graph for the Research of Cybersecurity" @default.
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- W3208596824 doi "https://doi.org/10.1007/978-981-16-6471-7_28" @default.
- W3208596824 hasPublicationYear "2021" @default.
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