Attributed Heterogeneous Graph Neural Network for Malicious Domain Detection

2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD)(2021)

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Abstract
Malicious activities on the Internet are one of the most dangerous threats to users and organizations. Because of the flexibility a nd accessibility of domains, cyber criminals often utilize them to launch cyber attacks such as phishing or malware. Most of traditional malicious domain detection methods rely on feature engineering to learn the patterns of malicious domains. However, these methods c...
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Key words
malicious domain detection,attributed heterogeneous graph neural network,semi-supervised learning,cyber security
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