International Journal For Multidisciplinary Research

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Malicious URL Detection using Machine Learning and CSV

Author(s) Pranav Tripathi, Ms. Syama Krishna
Country India
Abstract The proposed program item is at risk to meet the security possibly filtering pernicious URLs. This is Python based code, a client is provoked to enter a URL, which is at that point put away in a CSV record. The code peruses the CSV record, extricates different highlights of the URL such as length, the number of characters, and the number of registries in the URL. It too checks whether the URL employments an IP address or a abbreviated URL benefit. These highlights can be utilized for URL classification and distinguishing proof of possibly malevolent URLs.
The expanding predominance of cyber dangers such as phishing, malware dispersion, and other malevolent exercises has made URL discovery basic for online security. This consider presents a Python-based device for identifying possibly pernicious URLs by analyzing basic highlights such as URL length, character check, and catalog profundity. The device leverages machine learning models to classify URLs based on these highlights. Test comes about illustrate that the proposed strategy can successfully recognize between generous and pernicious URLs, giving a strong arrangement for upgrading cybersecurity.
Keywords python, csv, url highlights, url classification.
Field Computer > Network / Security
Published In Volume 6, Issue 4, July-August 2024
Published On 2024-08-27
Cite This Malicious URL Detection using Machine Learning and CSV - Pranav Tripathi, Ms. Syama Krishna - IJFMR Volume 6, Issue 4, July-August 2024. DOI 10.36948/ijfmr.2024.v06i04.26425
DOI https://doi.org/10.36948/ijfmr.2024.v06i04.26425
Short DOI https://doi.org/gt8g5v
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