Detecting Malicious URLs
收藏DataCite Commons2020-09-20 更新2025-04-09 收录
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https://www.impactcybertrust.org/dataset_view?idDataset=938
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The long-term goal of this research is to construct a real-time system that uses machine learning techniques to detect malicious URLs (spam, phishing, exploits, and so on). This dataset shows the recorded attempts to use machine learning to detect malicious URLs. UCSD explored techniques that involve classifying URLs based on their lexical and host-based features, as well as online learning to process large numbers of examples and adapt quickly to evolving URLs over time. The data set consists of about 2.4 million URLs (examples) and 3.2 million features. ; csestudent@eng.ucsd.edu
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IMPACT创建时间：
2018-10-25
