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tags: |
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- MALICIOUS |
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- URL |
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- DOMAIN |
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- ANALYSIS |
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- ML |
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- IOT |
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--- |
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### URL Classification Dataset |
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# About Dataset |
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Malicious URLs or malicious websites pose a serious threat to cybersecurity. They host unsolicited content, such as spam, phishing, and drive-by downloads, luring unsuspecting users into scams that lead to monetary loss, theft of private information, and malware installation. These threats result in billions of dollars in losses annually. |
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This dataset has been collected to include a large number of examples of malicious URLs. The goal is to develop a machine learning-based model that can proactively identify and block malicious URLs before they infect computer systems or spread across the internet. |
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# Content |
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This dataset consists of 651,191 URLs, categorized as follows: |
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- 428,103 benign (safe) URLs |
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- 96,457 defacement URLs |
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- 94,111 phishing URLs |
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- 32,520 malware URLs |
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Curating a high-quality dataset is one of the most crucial steps in any machine learning project. This dataset has been carefully curated from five different sources to ensure diversity and reliability. |
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# Warning: |
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If the model is not accessible via Hugging Face, please download it from the repository:nishnk/url_classification_model.pkl |
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license: mitlanguage: |
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enmetrics: |
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accuracy |
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code_eval |
