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- W4224110802 abstract "Web attacks such as spamming, phishing, and malware are common on the Internet. When an unsuspecting user hits the URL, the user becomes a victim of the assaults, which have significant consequences for commercial, finance, and social networking sites. Lexical features, host-based features, content-based features, DNS features, popularity features, and other discriminative features are used to generate a decent feature representation of the URL. URL dataset is collected from ISCX-URL. The goal of this research is to create a multi-class classification model that can categorise URLs as a possible threat to system security by combining several criteria to get the optimal Machine Learning Model." @default.
- W4224110802 created "2022-04-19" @default.
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- W4224110802 date "2022-03-29" @default.
- W4224110802 modified "2023-09-26" @default.
- W4224110802 title "Implementing Multiclass Classification to find the Optimal Machine Learning Model for Forecasting Malicious URLs" @default.
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- W4224110802 doi "https://doi.org/10.1109/iccmc53470.2022.9754005" @default.
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