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The experimental results show that the proposed URL attributes and behavior can help improve the ability to detect malicious URL significantly. This is suggested that the proposed system may be considered as anoptimized and friendly used solution for malicious URL detection.", "lang": "en" }, "license": { "url": "https://creativecommons.org/licenses/by/4.0", "start": "2023-08-07", "applies_to": "vor" }, "references": [ { "key": "ref1", "unstructured": "D. Sahoo, C. Liu, S.C.H. Hoi, “Malicious URL Detection using Machine Learning: A Survey”. CoRR, abs/1701.07179, 2017" }, { "key": "ref2", "doi": "10.1109/surv.2013.032213.00009", "unstructured": "M. Khonji, Y. Iraqi, and A. Jones, “Phishing detection: a literature survey,” IEEE Communications Surveys & Tutorials, vol. 15, no. 4, pp. 2091–2121, 2013" }, { "key": "ref3", "doi": "10.1145/1772690.1772720", "unstructured": "M. Cova, C. Kruegel, and G. 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