Malicious URL Detection Using Machine Learning Algorithms
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| Auteurs principaux: | , , , , |
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| Format: | Recurso digital |
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Zenodo
2026
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| _version_ | 1866901548885344256 |
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| author | Ashish Nanotkar Kaustubh Duke Ketan Pisalkar Sushil Bhakne Aditya Wadewale |
| author_facet | Ashish Nanotkar Kaustubh Duke Ketan Pisalkar Sushil Bhakne Aditya Wadewale |
| contents | <p>The expansive adoption of the internet has led to a surge in cyber risks, such as phishing, malware dissemination, and online scams, with malicious URLs frequently serving as the primary delivery mechanism for these attacks. Traditional blacklist systems are often inadequate because they cannot keep pace with the constant generation and modification of harmful links. This research introduces an efficient, machine learning-driven framework for the identification of malicious URLs. The method presented focuses on analyzing the inherent lexical and structural properties of a URL, avoiding the need to inspect webpage content, thereby significantly speeding up the detection process. The system employs multiple machine learning models, trained on these characteristics, to categorize URLs as either safe or dangerous. Engineered for real-time operation, the framework is capable of processing vast quantities of URL data. Evaluation experiments confirm that the proposed technique delivers strong accuracy alongside a minimal rate of false positives. This system can be integrated into web browsers, email defense platforms, and network security infrastructure to bolster overall digital safety.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18507798 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Malicious URL Detection Using Machine Learning Algorithms Ashish Nanotkar Kaustubh Duke Ketan Pisalkar Sushil Bhakne Aditya Wadewale <p>The expansive adoption of the internet has led to a surge in cyber risks, such as phishing, malware dissemination, and online scams, with malicious URLs frequently serving as the primary delivery mechanism for these attacks. Traditional blacklist systems are often inadequate because they cannot keep pace with the constant generation and modification of harmful links. This research introduces an efficient, machine learning-driven framework for the identification of malicious URLs. The method presented focuses on analyzing the inherent lexical and structural properties of a URL, avoiding the need to inspect webpage content, thereby significantly speeding up the detection process. The system employs multiple machine learning models, trained on these characteristics, to categorize URLs as either safe or dangerous. Engineered for real-time operation, the framework is capable of processing vast quantities of URL data. Evaluation experiments confirm that the proposed technique delivers strong accuracy alongside a minimal rate of false positives. This system can be integrated into web browsers, email defense platforms, and network security infrastructure to bolster overall digital safety.</p> |
| title | Malicious URL Detection Using Machine Learning Algorithms |
| url | https://doi.org/10.5281/zenodo.18507798 |