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- W3091625377 abstract "In recent years, due to the increasing complexity of malware and serious threats to system security, the anti-malware industry and researchers urgently need new technologies to improve the abilities to prevent malicious attacks. On this basis, this paper proposes a new malware detection method, which describes malware in terms of content-based and relationship-based features. Firstly, different types of entities (i.e., PE file, API, DLL, Registry, Mutex) and their rich semantic relationships (i.e., PE File-API, PE File-DLL, PE File-Registry, PE File-Mutex, DLL-API) are built into a model, which is named heterogeneous information network (HIN). Based on the constructed meta-path scheme, metapath2vec embedding model is used to learn the HIN low-dimensional vectors, which can capture the structure and semantic relationship of HIN. Finally, A Convolutional Neural Network (CNN) is designed to classify the learned HIN representation. The experimental results show that this method achieves 93% accuracy." @default.
- W3091625377 created "2020-10-08" @default.
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- W3091625377 date "2020-06-01" @default.
- W3091625377 modified "2023-09-23" @default.
- W3091625377 title "Malware classification based on heterogeneous information network representation learning" @default.
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- W3091625377 doi "https://doi.org/10.1109/icbaie49996.2020.00018" @default.
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