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- W3041736503 abstract "Summary The security threats from malicious web pages have become a hot topic for cyber security. One goal pursued by current research is to identify malicious web pages quickly, accurately, and efficiently. Considering the high detection costs and potential dimensionality curse of malicious webpage detection, in this article, we proposes a detection framework based on feature optimization and hybrid classification. It provides three properties: more new malicious webpage features, information gain‐based feature selection method, and integrating multiple machine learning method. A comprehensive experimental evaluation demonstrates that the proposed framework has remarkable advantages in aspects of detection accuracy and detection performance." @default.
- W3041736503 created "2020-07-16" @default.
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- W3041736503 date "2020-07-06" @default.
- W3041736503 modified "2023-10-15" @default.
- W3041736503 title "Feature optimization and hybrid classification for malicious web page detection" @default.
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- W3041736503 doi "https://doi.org/10.1002/cpe.5859" @default.
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